,id,category,og_question,code,question,correct_ans 0,2,area_based,Which state (excluding UTs) has the lowest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) exhibits the minimum PM2.5 concentration per square kilometer, based on average PM2.5 values?",Maharashtra 1,3,area_based,Which state (excluding UTs) has the 3rd lowest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the 3rd lowest PM2.5 concentration per square kilometer, according to median PM2.5 values?",Karnataka 2,5,area_based,Which state (excluding UTs) has the lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the minimum PM2.5 concentration per square kilometer, using 75th percentile PM2.5 values?",Karnataka 3,7,area_based,Which state (excluding UTs) has the lowest PM 2.5 concentration per square kilometer based on the 25th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the lowest PM2.5 concentration per square kilometer, according to 25th percentile PM2.5 values?",Maharashtra 4,8,area_based,Which state (excluding UTs) has the highest PM 2.5 concentration per square kilometer based on the total PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the highest PM2.5 concentration per square kilometer, based on total PM2.5 values?",Haryana 5,9,area_based,Which state (excluding UTs) has the lowest PM 2.5 concentration per square kilometer based on the total PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the minimum PM2.5 concentration per square kilometer, using total PM2.5 values?",Arunachal Pradesh 6,11,area_based,Which state (excluding UTs) has the 2nd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the 2nd minimum PM2.5 concentration per square kilometer, according to 75th percentile PM2.5 values?",Madhya Pradesh 7,12,area_based,Which state (excluding UTs) has the 2nd highest PM 10 concentration per square kilometer based on the total PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest PM10 concentration per square kilometer, based on total PM10 values?",Bihar 8,13,area_based,Which state (excluding UTs) has the 2nd lowest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the 2nd minimum PM10 concentration per square kilometer, using the standard deviation of PM10 values?",Rajasthan 9,14,area_based,Which state (excluding UTs) has the 3rd highest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) exhibits the 3rd maximum PM10 concentration per square kilometer, based on 25th percentile PM10 values?",Haryana 10,15,area_based,Which state (excluding UTs) has the 2nd highest PM 10 concentration per square kilometer based on the 75th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the 2nd highest PM10 concentration per square kilometer, according to 75th percentile PM10 values?",Nagaland 11,17,area_based,Which state (excluding UTs) has the 2nd lowest PM 10 concentration per square kilometer based on the total PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the 2nd minimum PM10 concentration per square kilometer, using total PM10 values?",Himachal Pradesh 12,18,area_based,Which state (excluding UTs) has the 3rd highest PM 2.5 concentration per square kilometer based on the standard deviation of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) exhibits the 3rd highest PM2.5 concentration per square kilometer, based on the standard deviation of PM2.5 values?",Sikkim 13,19,area_based,Which state (excluding UTs) has the 2nd highest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the 2nd maximum PM2.5 concentration per square kilometer, according to average PM2.5 values?",Nagaland 14,20,area_based,Which state (excluding UTs) has the 2nd highest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest PM10 concentration per square kilometer, based on the standard deviation of PM10 values?",Sikkim 15,21,area_based,Which state (excluding UTs) has the lowest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the minimum PM10 concentration per square kilometer, using 25th percentile PM10 values?",Maharashtra 16,22,area_based,Which state (excluding UTs) has the 2nd lowest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) exhibits the 2nd lowest PM2.5 concentration per square kilometer, based on median PM2.5 values?",Maharashtra 17,24,area_based,Which state (excluding UTs) has the 2nd lowest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd lowest PM2.5 concentration per square kilometer, based on average PM2.5 values?",Madhya Pradesh 18,25,area_based,Which state (excluding UTs) has the lowest PM 10 concentration per square kilometer based on the variance of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].var().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the minimum PM10 concentration per square kilometer, using the variance of PM10 values?",Karnataka 19,27,area_based,Which state (excluding UTs) has the lowest PM 10 concentration per square kilometer based on the total PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the lowest PM10 concentration per square kilometer, according to total PM10 values?",Arunachal Pradesh 20,28,area_based,Which state (excluding UTs) has the 2nd highest PM 10 concentration per square kilometer based on the median PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd maximum PM10 concentration per square kilometer, based on median PM10 values?",Nagaland 21,29,area_based,Which state (excluding UTs) has the 2nd lowest PM 10 concentration per square kilometer based on the median PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the 2nd minimum PM10 concentration per square kilometer, using median PM10 values?",Maharashtra 22,30,area_based,Which state (excluding UTs) has the 2nd lowest PM 2.5 concentration per square kilometer based on the standard deviation of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) exhibits the 2nd lowest PM2.5 concentration per square kilometer, based on the standard deviation of PM2.5 values?",Rajasthan 23,32,area_based,Which state (excluding UTs) has the 3rd highest PM 10 concentration per square kilometer based on the 75th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd highest PM10 concentration per square kilometer, based on 75th percentile PM10 values?",Sikkim 24,33,area_based,Which state (excluding UTs) has the highest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) shows the maximum PM2.5 concentration per square kilometer, using median PM2.5 values?",Tripura 25,35,area_based,Which state (excluding UTs) has the lowest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) presents the minimum PM10 concentration per square kilometer, according to the standard deviation of PM10 values?",Maharashtra 26,36,area_based,Which union territory has the 3rd highest PM 10 concentration per square kilometer based on the median PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which union territory shows the 3rd maximum PM10 concentration per square kilometer, using median PM10 values?",Puducherry 27,37,area_based,Which union territory has the lowest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory exhibits the minimum PM2.5 concentration per square kilometer, based on average PM2.5 values?",Jammu and Kashmir 28,38,area_based,Which union territory has the 3rd lowest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state'] print(max_area_state) true_code() ","Which union territory presents the 3rd lowest PM2.5 concentration per square kilometer, according to median PM2.5 values?",Delhi 29,39,area_based,Which union territory has the highest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the highest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?",Chandigarh 30,41,area_based,Which union territory has the 3rd highest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd maximum PM2.5 concentration per square kilometer, based on average PM2.5 values?",Puducherry 31,42,area_based,Which union territory has the lowest PM 2.5 concentration per square kilometer based on the 25th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory presents the lowest PM2.5 concentration per square kilometer, according to 25th percentile PM2.5 values?",Jammu and Kashmir 32,44,area_based,Which union territory has the lowest PM 2.5 concentration per square kilometer based on the total PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory shows the minimum PM2.5 concentration per square kilometer, using total PM2.5 values?",Jammu and Kashmir 33,45,area_based,Which union territory has the 3rd lowest PM 10 concentration per square kilometer based on the variance of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].var().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd lowest PM10 concentration per square kilometer, based on the variance of PM10 values?",Delhi 34,48,area_based,Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory shows the 2nd minimum PM10 concentration per square kilometer, using the standard deviation of PM10 values?",Puducherry 35,49,area_based,Which union territory has the 3rd highest PM 10 concentration per square kilometer based on the 25th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd maximum PM10 concentration per square kilometer, based on 25th percentile PM10 values?",Puducherry 36,50,area_based,Which union territory has the 2nd highest PM 10 concentration per square kilometer based on the 75th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory presents the 2nd highest PM10 concentration per square kilometer, according to 75th percentile PM10 values?",Delhi 37,51,area_based,Which union territory has the 2nd highest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd maximum PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?",Delhi 38,52,area_based,Which union territory has the 2nd lowest PM 10 concentration per square kilometer based on the total PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory shows the 2nd minimum PM10 concentration per square kilometer, using total PM10 values?",Puducherry 39,53,area_based,Which union territory has the 3rd highest PM 2.5 concentration per square kilometer based on the standard deviation of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd highest PM2.5 concentration per square kilometer, based on the standard deviation of PM2.5 values?",Puducherry 40,54,area_based,Which union territory has the 2nd highest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory presents the 2nd maximum PM2.5 concentration per square kilometer, according to average PM2.5 values?",Delhi 41,55,area_based,Which union territory has the 2nd highest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd highest PM10 concentration per square kilometer, based on the standard deviation of PM10 values?",Delhi 42,59,area_based,Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the average PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd lowest PM2.5 concentration per square kilometer, based on average PM2.5 values?",Puducherry 43,60,area_based,Which union territory has the lowest PM 10 concentration per square kilometer based on the variance of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].var().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory shows the minimum PM10 concentration per square kilometer, using the variance of PM10 values?",Jammu and Kashmir 44,61,area_based,Which union territory has the 3rd lowest PM 2.5 concentration per square kilometer based on the 75th percentile of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on 75th percentile PM2.5 values?",Delhi 45,62,area_based,Which union territory has the lowest PM 10 concentration per square kilometer based on the total PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].sum().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory presents the lowest PM10 concentration per square kilometer, according to total PM10 values?",Jammu and Kashmir 46,63,area_based,Which union territory has the 2nd highest PM 10 concentration per square kilometer based on the median PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd maximum PM10 concentration per square kilometer, based on median PM10 values?",Delhi 47,65,area_based,Which union territory has the 2nd lowest PM 2.5 concentration per square kilometer based on the standard deviation of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 2nd lowest PM2.5 concentration per square kilometer, based on the standard deviation of PM2.5 values?",Puducherry 48,66,area_based,Which union territory has the 2nd highest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory presents the 2nd maximum PM2.5 concentration per square kilometer, according to median PM2.5 values?",Delhi 49,67,area_based,Which union territory has the 3rd highest PM 10 concentration per square kilometer based on the 75th percentile of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which union territory has the 3rd highest PM10 concentration per square kilometer, based on 75th percentile PM10 values?",Puducherry 50,68,area_based,Which union territory has the highest PM 2.5 concentration per square kilometer based on the median PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory shows the maximum PM2.5 concentration per square kilometer, using median PM2.5 values?",Chandigarh 51,69,area_based,Which union territory has the 3rd lowest PM 2.5 concentration per square kilometer based on the variance of PM 2.5 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM2.5'].var().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM2.5'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[2]['state'] print(max_area_state) true_code() ","Which union territory exhibits the 3rd lowest PM2.5 concentration per square kilometer, based on the variance of PM2.5 values?",Delhi 52,70,area_based,Which union territory has the lowest PM 10 concentration per square kilometer based on the standard deviation of PM 10 values?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25 = main_data.groupby('state')['PM10'].std().reset_index() states_area = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm25.merge(states_area, on='state', how='inner') merged_df['pm_per_km2'] = merged_df['PM10'] / merged_df['area (km2)'] max_area_state = merged_df.sort_values('pm_per_km2').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory presents the minimum PM10 concentration per square kilometer, according to the standard deviation of PM10 values?",Jammu and Kashmir 53,71,area_based,Which state has the highest number of monitoring stations relative to its area?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() filtered_states_data = states_data[['state', 'area (km2)']] merged_df = station_counts.merge(filtered_states_data, on='state', how='inner') merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)'] required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which state possesses the highest number of monitoring stations in proportion to its area?,Delhi 54,72,area_based,Which state has the 3rd highest number of monitoring stations relative to its area?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() filtered_states_data = states_data[['state', 'area (km2)']] merged_df = station_counts.merge(filtered_states_data, on='state', how='inner') merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)'] required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Which state has the 3rd largest count of monitoring stations compared to its area?,Puducherry 55,75,area_based,Which union territory has the highest number of monitoring stations relative to its area?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = station_counts.merge(filtered_states_data, on='state', how='inner') merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)'] required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which union territory possesses the highest number of monitoring stations in proportion to its area?,Delhi 56,76,area_based,Which union territory has the 3rd highest number of monitoring stations relative to its area?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = station_counts.merge(filtered_states_data, on='state', how='inner') merged_df['stations_per_km2'] = merged_df['station'] / merged_df['area (km2)'] required_state = merged_df.sort_values('stations_per_km2', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Which union territory has the 3rd largest count of monitoring stations compared to its area?,Puducherry 57,80,area_based,Report the total land area of the state (excluding UTs) with the 3rd highest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined', ascending=False).iloc[2]['area (km2)'] print(required_area) true_code() ",State the total land area of the state (excluding Union Territories) with the 3rd highest combined PM2.5 and PM10 concentrations.,240928 58,81,area_based,Report the total land area of the state (excluding UTs) with the lowest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined').iloc[0]['area (km2)'] print(required_area) true_code() ",Report the total land area of the state (excluding Union Territories) showing the minimum combined PM2.5 and PM10 concentrations.,7096 59,82,area_based,Report the total land area of the state (excluding UTs) with the 4th highest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined', ascending=False).iloc[3]['area (km2)'] print(required_area) true_code() ",Provide the total land area of the state (excluding Union Territories) having the 4th maximum combined PM2.5 and PM10 concentrations.,55673 60,84,area_based,Report the total land area of the union territory with the 3rd highest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined', ascending=False).iloc[2]['area (km2)'] print(required_area) true_code() ",Report the total land area of the union territory showing the 3rd maximum combined PM2.5 and PM10 concentrations.,42241 61,85,area_based,Report the total land area of the union territory with the lowest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined').iloc[0]['area (km2)'] print(required_area) true_code() ",Provide the total land area of the union territory having the minimum combined PM2.5 and PM10 concentrations.,479 62,86,area_based,Report the total land area of the union territory with the 4th highest combined PM2.5 and PM10 concentrations.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_averages = main_data.groupby('state')[['PM2.5', 'PM10']].mean() state_averages['combined'] = state_averages['PM2.5'] + state_averages['PM10'] filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_averages.merge(filtered_states_data, on='state', how='inner') required_area = merged_df.sort_values('combined', ascending=False).iloc[3]['area (km2)'] print(required_area) true_code() ",State the total land area of the union territory with the 4th highest combined PM2.5 and PM10 concentrations.,479 63,87,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd largest land area among the top 10 most polluted states, according to the 25th percentile of PM10 levels?",Madhya Pradesh 64,88,area_based,"Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 3 most polluted states, based on the standard deviation of PM2.5 levels?",Manipur 65,89,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 5 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 5 most polluted states, according to the 75th percentile of PM10 levels?",Himachal Pradesh 66,90,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 3 most polluted states, based on total PM2.5 levels?",Maharashtra 67,92,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 5 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 5 most polluted states, based on average PM10 levels?",Bihar 68,93,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 10 most polluted states, according to median PM10 levels?",Himachal Pradesh 69,96,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the minimum land area among the top 10 most polluted states, based on median PM2.5 levels?",Tripura 70,97,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 5 most polluted states, according to total PM2.5 levels?",Rajasthan 71,98,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 3 most polluted states, based on the variance of PM2.5 levels?",Manipur 72,99,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd highest land area among the top 5 most polluted states, according to median PM2.5 levels?",Jharkhand 73,100,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 10 most polluted states, based on total PM10 levels?",Madhya Pradesh 74,101,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the highest land area among the top 5 most polluted states, according to the 75th percentile of PM2.5 levels?",Uttar Pradesh 75,103,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 10 most polluted states, according to average PM2.5 levels?",Himachal Pradesh 76,104,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 5 most polluted states, based on average PM10 levels?",Haryana 77,105,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 5 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 5 most polluted states, according to median PM10 levels?",Himachal Pradesh 78,106,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 5 most polluted states, based on the 25th percentile of PM2.5 levels?",Haryana 79,107,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 3 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 3 most polluted states, according to median PM2.5 levels?",Bihar 80,108,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 5 most polluted states, based on total PM10 levels?",Haryana 81,111,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to total PM10 levels?",Haryana 82,112,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 10 most polluted states, based on total PM2.5 levels?",Maharashtra 83,113,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 5 most polluted states, according to total PM2.5 levels?",Uttar Pradesh 84,114,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 3 most polluted states, based on the variance of PM10 levels?",Assam 85,116,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 10 most polluted states, based on the variance of PM2.5 levels?",Uttar Pradesh 86,117,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest land area among the top 5 most polluted states, according to average PM2.5 levels?",Bihar 87,118,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 3 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 3 most polluted states, based on the 75th percentile of PM2.5 levels?",Bihar 88,119,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to the 75th percentile of PM10 levels?",Himachal Pradesh 89,121,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd highest land area among the top 3 most polluted states, according to median PM10 levels?",Haryana 90,122,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 5 most polluted states, based on the 25th percentile of PM10 levels?",Haryana 91,123,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 5 most polluted states, according to the 25th percentile of PM10 levels?",Himachal Pradesh 92,124,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 5 most polluted states, based on total PM2.5 levels?",Haryana 93,126,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 5 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 5 most polluted states, based on average PM2.5 levels?",Assam 94,128,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 3 most polluted states, based on the standard deviation of PM2.5 levels?",Bihar 95,129,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest land area among the top 10 most polluted states, according to median PM2.5 levels?",Uttar Pradesh 96,130,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 5 most polluted states, based on median PM10 levels?",Uttar Pradesh 97,131,area_based,"Which state(excuding UTs) has the highest land area among the top 3 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 3 most polluted states, according to median PM10 levels?",Bihar 98,132,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 3 most polluted states, based on the 25th percentile of PM2.5 levels?",Jharkhand 99,134,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the largest land area among the top 5 most polluted states, based on the 25th percentile of PM2.5 levels?",Bihar 100,135,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest land area among the top 5 most polluted states, according to median PM2.5 levels?",Bihar 101,136,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 3 most polluted states, based on the standard deviation of PM10 levels?",Uttar Pradesh 102,137,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to the 25th percentile of PM2.5 levels?",Tripura 103,139,area_based,"Which state(excuding UTs) has the highest land area among the top 3 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 3 most polluted states, according to the 75th percentile of PM10 levels?",Uttar Pradesh 104,140,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 10 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 10 most polluted states, based on the variance of PM2.5 levels?",Haryana 105,141,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd highest land area among the top 3 most polluted states, according to the standard deviation of PM2.5 levels?",Manipur 106,142,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 5 most polluted states, based on the standard deviation of PM10 levels?",Bihar 107,143,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest land area among the top 5 most polluted states, according to total PM2.5 levels?",Maharashtra 108,144,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 3 most polluted states, based on the 25th percentile of PM2.5 levels?",Haryana 109,145,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 5 most polluted states, according to the 25th percentile of PM10 levels?",Jharkhand 110,147,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 3 most polluted states, according to median PM2.5 levels?",Uttar Pradesh 111,148,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 3 most polluted states, based on average PM2.5 levels?",Bihar 112,149,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 3 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 3 most polluted states, according to the 75th percentile of PM10 levels?",Bihar 113,150,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 10 most polluted states, based on total PM2.5 levels?",Madhya Pradesh 114,151,area_based,"Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 10 most polluted states, according to the variance of PM10 levels?",Rajasthan 115,152,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 10 most polluted states, based on the variance of PM10 levels?",Uttar Pradesh 116,155,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to average PM2.5 levels?",Haryana 117,157,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 5 most polluted states, according to the 75th percentile of PM10 levels?",Uttar Pradesh 118,158,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 10 most polluted states, based on the standard deviation of PM10 levels?",Haryana 119,159,area_based,"Which state(excuding UTs) has the highest land area among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 10 most polluted states, according to total PM10 levels?",Rajasthan 120,160,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 10 most polluted states, based on average PM10 levels?",Punjab 121,161,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 10 most polluted states, according to the standard deviation of PM2.5 levels?",Manipur 122,162,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 10 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 10 most polluted states, based on the variance of PM2.5 levels?",Manipur 123,163,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd highest land area among the top 10 most polluted states, according to average PM10 levels?",Uttar Pradesh 124,164,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 3 most polluted states, based on average PM2.5 levels?",Bihar 125,165,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to average PM10 levels?",Haryana 126,166,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd smallest land area among the top 3 most polluted states, based on average PM2.5 levels?",Uttar Pradesh 127,167,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 10 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd largest land area among the top 10 most polluted states, according to the 75th percentile of PM10 levels?",Madhya Pradesh 128,168,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 5 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 5 most polluted states, based on the variance of PM10 levels?",Jharkhand 129,169,area_based,"Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the smallest land area among the top 3 most polluted states, according to total PM10 levels?",Haryana 130,170,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the largest land area among the top 5 most polluted states, based on average PM2.5 levels?",Uttar Pradesh 131,171,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 3 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 3 most polluted states, according to the variance of PM2.5 levels?",Uttar Pradesh 132,172,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 5 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 5 most polluted states, based on median PM10 levels?",Bihar 133,173,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd minimum land area among the top 5 most polluted states, according to the 25th percentile of PM2.5 levels?",Punjab 134,174,area_based,"Which state(excuding UTs) has the highest land area among the top 5 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the largest land area among the top 5 most polluted states, based on the variance of PM10 levels?",Uttar Pradesh 135,175,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 2nd highest land area among the top 3 most polluted states, according to the 25th percentile of PM10 levels?",Himachal Pradesh 136,176,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 3 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 3 most polluted states, based on average PM10 levels?",Bihar 137,178,area_based,"Which state(excuding UTs) has the 2nd highest land area among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd largest land area among the top 5 most polluted states, based on the 25th percentile of PM2.5 levels?",Jharkhand 138,179,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the minimum land area among the top 10 most polluted states, according to median PM10 levels?",Haryana 139,180,area_based,"Which state(excuding UTs) has the lowest land area among the top 3 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 3 most polluted states, based on the 75th percentile of PM2.5 levels?",Haryana 140,182,area_based,"Which state(excuding UTs) has the lowest land area among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the smallest land area among the top 10 most polluted states, based on average PM2.5 levels?",Tripura 141,183,area_based,"Which state(excuding UTs) has the highest land area among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 3 most polluted states, according to average PM2.5 levels?",Uttar Pradesh 142,184,area_based,"Which state(excuding UTs) has the 3rd highest land area among the top 3 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 3rd largest land area among the top 3 most polluted states, based on the 25th percentile of PM10 levels?",Haryana 143,185,area_based,"Which state(excuding UTs) has the lowest land area among the top 5 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the smallest land area among the top 5 most polluted states, according to the variance of PM2.5 levels?",Manipur 144,186,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 3 most polluted states, based on the variance of PM10 levels?",Bihar 145,187,area_based,"Which state(excuding UTs) has the 3rd lowest land area among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[2]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the 3rd minimum land area among the top 5 most polluted states, according to median PM2.5 levels?",Jharkhand 146,188,area_based,"Which state(excuding UTs) has the 2nd lowest land area among the top 5 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) possesses the 2nd smallest land area among the top 5 most polluted states, based on the standard deviation of PM2.5 levels?",Haryana 147,189,area_based,"Which state(excuding UTs) has the highest land area among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which state (excluding Union Territories) has the largest land area among the top 3 most polluted states, according to the variance of PM10 levels?",Uttar Pradesh 148,190,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the largest land area among the top 2 most polluted union territories, based on the 75th percentile of PM2.5 levels?",Delhi 149,191,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to the variance of PM10 levels?",Delhi 150,192,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the smallest land area among the top 2 most polluted union territories, based on total PM2.5 levels?",Chandigarh 151,193,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the largest land area among the top 4 most polluted union territories, according to total PM10 levels?",Jammu and Kashmir 152,197,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the minimum land area among the top 2 most polluted union territories, according to the 25th percentile of PM10 levels?",Chandigarh 153,198,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the smallest land area among the top 2 most polluted union territories, based on the 25th percentile of PM2.5 levels?",Chandigarh 154,199,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the largest land area among the top 4 most polluted union territories, according to median PM2.5 levels?",Jammu and Kashmir 155,200,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd smallest land area among the top 4 most polluted union territories, based on median PM10 levels?",Puducherry 156,201,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the largest land area among the top 4 most polluted union territories, according to the 75th percentile of PM10 levels?",Jammu and Kashmir 157,202,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd smallest land area among the top 2 most polluted union territories, based on the 75th percentile of PM2.5 levels?",Delhi 158,203,area_based,"Which union territory has the 2nd highest land area among the top 2 most polluted union territories, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd highest land area among the top 2 most polluted union territories, according to the 75th percentile of PM10 levels?",Chandigarh 159,204,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the smallest land area among the top 2 most polluted union territories, based on average PM2.5 levels?",Chandigarh 160,205,area_based,"Which union territory has the lowest land area among the top 4 most polluted union territories, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the minimum land area among the top 4 most polluted union territories, according to total PM2.5 levels?",Chandigarh 161,206,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the largest land area among the top 4 most polluted union territories, based on the 25th percentile of PM10 levels?",Jammu and Kashmir 162,207,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 4 most polluted union territories, according to the 25th percentile of PM2.5 levels?",Puducherry 163,208,area_based,"Which union territory has the 2nd highest land area among the top 4 most polluted union territories, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd largest land area among the top 4 most polluted union territories, based on total PM10 levels?",Delhi 164,210,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the largest land area among the top 2 most polluted union territories, based on the variance of PM2.5 levels?",Delhi 165,211,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the minimum land area among the top 2 most polluted union territories, according to the 75th percentile of PM2.5 levels?",Chandigarh 166,212,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the largest land area among the top 2 most polluted union territories, based on median PM10 levels?",Delhi 167,213,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to the 25th percentile of PM2.5 levels?",Delhi 168,214,area_based,"Which union territory has the 2nd highest land area among the top 2 most polluted union territories, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd largest land area among the top 2 most polluted union territories, based on the standard deviation of PM2.5 levels?",Chandigarh 169,215,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to the standard deviation of PM2.5 levels?",Delhi 170,216,area_based,"Which union territory has the lowest land area among the top 4 most polluted union territories, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the smallest land area among the top 4 most polluted union territories, based on the variance of PM2.5 levels?",Chandigarh 171,217,area_based,"Which union territory has the 2nd highest land area among the top 2 most polluted union territories, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd highest land area among the top 2 most polluted union territories, according to average PM10 levels?",Chandigarh 172,218,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd smallest land area among the top 4 most polluted union territories, based on the 25th percentile of PM10 levels?",Puducherry 173,222,area_based,"Which union territory has the highest land area among the top 4 most polluted union territories, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the highest land area among the top 4 most polluted union territories, based on median PM10 levels?",Jammu and Kashmir 174,223,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the minimum land area among the top 2 most polluted union territories, according to the variance of PM10 levels?",Chandigarh 175,224,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory possesses the largest land area among the top 2 most polluted union territories, based on total PM10 levels?",Delhi 176,225,area_based,"Which union territory has the 2nd lowest land area among the top 2 most polluted union territories, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 2 most polluted union territories, according to average PM2.5 levels?",Delhi 177,227,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the largest land area among the top 2 most polluted union territories, according to average PM2.5 levels?",Delhi 178,228,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory possesses the 2nd smallest land area among the top 4 most polluted union territories, based on the variance of PM10 levels?",Puducherry 179,229,area_based,"Which union territory has the lowest land area among the top 2 most polluted union territories, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the minimum land area among the top 2 most polluted union territories, according to total PM10 levels?",Chandigarh 180,231,area_based,"Which union territory has the 2nd lowest land area among the top 4 most polluted union territories, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)').iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd minimum land area among the top 4 most polluted union territories, according to average PM2.5 levels?",Puducherry 181,233,area_based,"Which union territory has the highest land area among the top 2 most polluted union territories, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[0]['state'] print(max_area_state) true_code() ","Which union territory has the largest land area among the top 2 most polluted union territories, according to the standard deviation of PM10 levels?",Delhi 182,235,area_based,"Which union territory has the 2nd highest land area among the top 4 most polluted union territories, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_area = merged_df[merged_df['state'].isin(top_polluted_states)] max_area_state = top_states_area.sort_values('area (km2)', ascending=False).iloc[1]['state'] print(max_area_state) true_code() ","Which union territory has the 2nd highest land area among the top 4 most polluted union territories, according to the standard deviation of PM10 levels?",Delhi 183,236,area_based,"Which state with a land area greater than 50,000 km² has the lowest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the minimum PM10 level, based on its total PM10 level?",Arunachal Pradesh 184,237,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[4]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 5th lowest PM2.5 level, according to its average PM2.5 level?",Andhra Pradesh 185,239,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 2nd lowest PM2.5 level, according to its variance of PM2.5 level?",Sikkim 186,241,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the minimum PM2.5 level, according to its median PM2.5 level?",Mizoram 187,242,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd minimum PM10 level, based on its 25th percentile PM10 level?",Karnataka 188,243,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 2.5 level, based on 75th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 5th highest PM2.5 level, according to its 75th percentile PM2.5 level?",Himachal Pradesh 189,244,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd minimum PM10 level, based on its total PM10 level?",Mizoram 190,245,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 3rd highest PM2.5 level, according to its standard deviation of PM2.5 level?",Haryana 191,246,area_based,"Which state with a land area greater than 50,000 km² has the 2nd highest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 2nd maximum PM10 level, based on its 75th percentile PM10 level?",Uttar Pradesh 192,247,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the highest PM2.5 level, according to its median PM2.5 level?",Bihar 193,248,area_based,"Which state with a land area lesser than 50,000 km² has the 5th lowest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[4]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 5th minimum PM2.5 level, based on its standard deviation of PM2.5 level?",Nagaland 194,249,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the maximum PM10 level, according to its variance of PM10 level?",Bihar 195,252,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd minimum PM2.5 level, based on its standard deviation of PM2.5 level?",Puducherry 196,253,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd highest PM10 level, according to its 25th percentile PM10 level?",Bihar 197,254,area_based,"Which state with a land area lesser than 50,000 km² has the 5th lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 5th minimum PM10 level, based on its 25th percentile PM10 level?",Puducherry 198,255,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 5th lowest PM10 level, according to its 75th percentile PM10 level?",Chhattisgarh 199,257,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the maximum PM2.5 level, according to its variance of PM2.5 level?",Uttar Pradesh 200,258,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd maximum PM10 level, based on its total PM10 level?",Rajasthan 201,259,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 2nd lowest PM2.5 level, according to its median PM2.5 level?",Sikkim 202,260,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd minimum PM10 level, based on its average PM10 level?",Meghalaya 203,261,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 2nd lowest PM2.5 level, according to its standard deviation of PM2.5 level?",Sikkim 204,262,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd maximum PM10 level, based on its standard deviation of PM10 level?",Tripura 205,263,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd lowest PM10 level, according to its variance of PM10 level?",Tamil Nadu 206,264,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd minimum PM2.5 level, based on its variance of PM2.5 level?",Telangana 207,265,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 5th highest PM2.5 level, according to its standard deviation of PM2.5 level?",Himachal Pradesh 208,266,area_based,"Which state with a land area lesser than 50,000 km² has the 5th highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 5th maximum PM2.5 level, based on its total PM2.5 level?",Tripura 209,267,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd lowest PM10 level, according to its 75th percentile PM10 level?",Karnataka 210,268,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the maximum PM2.5 level, based on its total PM2.5 level?",Uttar Pradesh 211,269,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 10 level, based on median PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 3rd highest PM10 level, according to its median PM10 level?",Chandigarh 212,270,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd maximum PM2.5 level, based on its total PM2.5 level?",Haryana 213,271,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the minimum PM2.5 level, according to its total PM2.5 level?",Sikkim 214,272,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd highest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd maximum PM2.5 level, based on its 25th percentile PM2.5 level?",Haryana 215,273,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 5th highest PM10 level, according to its 75th percentile PM10 level?",Jharkhand 216,274,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd maximum PM2.5 level, based on its 25th percentile PM2.5 level?",Bihar 217,275,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 3rd lowest PM10 level, according to its 25th percentile PM10 level?",Mizoram 218,276,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd minimum PM2.5 level, based on its average PM2.5 level?",Sikkim 219,277,area_based,"Which state with a land area lesser than 50,000 km² has the 5th lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 5th lowest PM10 level, according to its standard deviation of PM10 level?",Nagaland 220,278,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd maximum PM2.5 level, based on its median PM2.5 level?",Chandigarh 221,280,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd maximum PM10 level, based on its variance of PM10 level?",Haryana 222,281,area_based,"Which state with a land area lesser than 50,000 km² has the highest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the highest PM2.5 level, according to its median PM2.5 level?",Delhi 223,282,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd minimum PM10 level, based on its standard deviation of PM10 level?",Tamil Nadu 224,283,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd highest PM2.5 level, according to its standard deviation of PM2.5 level?",Assam 225,284,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 5th minimum PM10 level, based on its 25th percentile PM10 level?",Chhattisgarh 226,285,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the minimum PM10 level, according to its average PM10 level?",Sikkim 227,286,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[4]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 5th minimum PM2.5 level, based on its total PM2.5 level?",Chhattisgarh 228,288,area_based,"Which state with a land area greater than 50,000 km² has the 5th lowest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 5th minimum PM10 level, based on its variance of PM10 level?",Chhattisgarh 229,290,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 2nd minimum PM10 level, based on its standard deviation of PM10 level?",Uttarakhand 230,291,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the maximum PM10 level, according to its standard deviation of PM10 level?",Bihar 231,292,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 5th maximum PM2.5 level, based on its total PM2.5 level?",Madhya Pradesh 232,294,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd maximum PM10 level, based on its variance of PM10 level?",Tripura 233,295,area_based,"Which state with a land area greater than 50,000 km² has the lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the minimum PM10 level, according to its standard deviation of PM10 level?",Arunachal Pradesh 234,296,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 2nd minimum PM2.5 level, based on its median PM2.5 level?",Karnataka 235,297,area_based,"Which state with a land area greater than 50,000 km² has the highest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the maximum PM2.5 level, according to its 25th percentile PM2.5 level?",Himachal Pradesh 236,299,area_based,"Which state with a land area lesser than 50,000 km² has the 5th highest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 5th highest PM2.5 level, according to its variance of PM2.5 level?",Chandigarh 237,300,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the minimum PM2.5 level, based on its standard deviation of PM2.5 level?",Mizoram 238,301,area_based,"Which state with a land area lesser than 50,000 km² has the 5th lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[4]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 5th lowest PM10 level, according to its 75th percentile PM10 level?",Manipur 239,302,area_based,"Which state with a land area greater than 50,000 km² has the lowest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the minimum PM2.5 level, based on its 25th percentile PM2.5 level?",Arunachal Pradesh 240,303,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd lowest PM2.5 level, according to its average PM2.5 level?",Karnataka 241,304,area_based,"Which state with a land area greater than 50,000 km² has the 5th highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 5th maximum PM10 level, based on its variance of PM10 level?",West Bengal 242,305,area_based,"Which state with a land area lesser than 50,000 km² has the highest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the highest PM10 level, according to its standard deviation of PM10 level?",Delhi 243,307,area_based,"Which state with a land area greater than 50,000 km² has the 3rd lowest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd lowest PM10 level, according to its total PM10 level?",Uttarakhand 244,308,area_based,"Which state with a land area lesser than 50,000 km² has the lowest PM 2.5 level, based on 75th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the minimum PM2.5 level, based on its 75th percentile PM2.5 level?",Mizoram 245,309,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd highest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 3rd highest PM10 level, according to its average PM10 level?",Chandigarh 246,310,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd highest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd maximum PM2.5 level, based on its median PM2.5 level?",Haryana 247,313,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 2nd lowest PM10 level, according to its average PM10 level?",Tamil Nadu 248,314,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 2nd minimum PM2.5 level, based on its average PM2.5 level?",Chhattisgarh 249,316,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd minimum PM10 level, based on its standard deviation of PM10 level?",Kerala 250,317,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 2nd lowest PM2.5 level, according to its total PM2.5 level?",Mizoram 251,318,area_based,"Which state with a land area lesser than 50,000 km² has the 2nd lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 2nd minimum PM10 level, based on its 25th percentile PM10 level?",Meghalaya 252,319,area_based,"Which state with a land area greater than 50,000 km² has the 2nd highest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 2nd highest PM10 level, according to its total PM10 level?",Maharashtra 253,320,area_based,"Which state with a land area lesser than 50,000 km² has the 3rd lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10').iloc[2]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the 3rd minimum PM10 level, based on its average PM10 level?",Mizoram 254,321,area_based,"Which state with a land area lesser than 50,000 km² has the 5th highest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[4]['state'] print(required_state) true_code() ","Which state with a land area below 50,000 km² shows the 5th highest PM10 level, according to its 25th percentile PM10 level?",Jammu and Kashmir 255,322,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 2.5 level, based on 75th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state having a land area exceeding 50,000 km² registers the 3rd maximum PM2.5 level, based on its 75th percentile PM2.5 level?",Assam 256,323,area_based,"Which state with a land area greater than 50,000 km² has the 2nd lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 2nd lowest PM10 level, according to its 75th percentile PM10 level?",Tamil Nadu 257,324,area_based,"Which state with a land area lesser than 50,000 km² has the highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the maximum PM10 level, based on its variance of PM10 level?",Delhi 258,325,area_based,"Which state with a land area greater than 50,000 km² has the 3rd highest PM 10 level, based on median PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].median().reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 50000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Which state with a land area greater than 50,000 km² shows the 3rd highest PM10 level, according to its median PM10 level?",Uttar Pradesh 259,326,area_based,"Which state with a land area lesser than 50,000 km² has the highest PM 2.5 level, based on 75th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_data = pd.merge(state_pm, states_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 50000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state having a land area less than 50,000 km² registers the maximum PM2.5 level, based on its 75th percentile PM2.5 level?",Delhi 260,329,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the minimum PM10 level, according to its total PM10 level?",Puducherry 261,330,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the lowest PM2.5 level, based on its variance of PM2.5 level?",Jammu and Kashmir 262,331,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd maximum PM2.5 level, according to its 25th percentile PM2.5 level?",Jammu and Kashmir 263,332,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the 2nd highest PM2.5 level, based on its average PM2.5 level?",Jammu and Kashmir 264,333,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the minimum PM10 level, according to its 75th percentile PM10 level?",Puducherry 265,334,area_based,"Which union territory with a land area greater than 1,000 km² has the highest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the maximum PM10 level, based on its 25th percentile PM10 level?",Delhi 266,335,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the minimum PM10 level, according to its variance of PM10 level?",Jammu and Kashmir 267,337,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd maximum PM2.5 level, according to its total PM2.5 level?",Jammu and Kashmir 268,338,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the highest PM2.5 level, based on its standard deviation of PM2.5 level?",Chandigarh 269,339,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd maximum PM10 level, according to its 75th percentile PM10 level?",Jammu and Kashmir 270,340,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the minimum PM10 level, based on its 25th percentile PM10 level?",Puducherry 271,341,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd lowest PM 10 level, based on 75th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the 2nd minimum PM10 level, according to its 75th percentile PM10 level?",Chandigarh 272,342,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the lowest PM2.5 level, based on its average PM2.5 level?",Jammu and Kashmir 273,343,area_based,"Which union territory with a land area greater than 1,000 km² has the highest PM 2.5 level, based on variance of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the maximum PM2.5 level, according to its variance of PM2.5 level?",Delhi 274,344,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the 2nd highest PM10 level, based on its variance of PM10 level?",Jammu and Kashmir 275,345,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the maximum PM10 level, according to its standard deviation of PM10 level?",Chandigarh 276,347,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the 2nd minimum PM10 level, according to its average PM10 level?",Chandigarh 277,349,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 10 level, based on 25th percentile of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the minimum PM10 level, according to its 25th percentile PM10 level?",Jammu and Kashmir 278,350,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM2.5 level, based on its standard deviation of PM2.5 level?",Delhi 279,351,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 10 level, based on standard deviation of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].std().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the minimum PM10 level, according to its standard deviation of PM10 level?",Puducherry 280,353,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 2.5 level, based on 75th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the minimum PM2.5 level, according to its 75th percentile PM2.5 level?",Puducherry 281,355,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd lowest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the 2nd minimum PM2.5 level, according to its median PM2.5 level?",Chandigarh 282,356,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 10 level, based on variance of PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the highest PM10 level, based on its variance of PM10 level?",Chandigarh 283,357,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the maximum PM10 level, according to its average PM10 level?",Chandigarh 284,358,area_based,"Which union territory with a land area greater than 1,000 km² has the highest PM 2.5 level, based on median PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the highest PM2.5 level, based on its median PM2.5 level?",Delhi 285,359,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd minimum PM2.5 level, according to its total PM2.5 level?",Delhi 286,360,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd highest PM 2.5 level, based on standard deviation of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the 2nd highest PM2.5 level, based on its standard deviation of PM2.5 level?",Jammu and Kashmir 287,362,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the 2nd lowest PM2.5 level, based on its 25th percentile PM2.5 level?",Delhi 288,363,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the minimum PM2.5 level, according to its average PM2.5 level?",Puducherry 289,364,area_based,"Which union territory with a land area lesser than 1,000 km² has the lowest PM 10 level, based on median PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].median().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10').iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the lowest PM10 level, based on its median PM10 level?",Puducherry 290,365,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd minimum PM2.5 level, according to its average PM2.5 level?",Delhi 291,366,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the highest PM10 level, based on its total PM10 level?",Chandigarh 292,367,area_based,"Which union territory with a land area greater than 1,000 km² has the lowest PM 2.5 level, based on 25th percentile of PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5').iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the minimum PM2.5 level, according to its 25th percentile PM2.5 level?",Jammu and Kashmir 293,368,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd highest PM 10 level, based on total PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory with a land area below 1,000 km² shows the 2nd highest PM10 level, based on its total PM10 level?",Puducherry 294,369,area_based,"Which union territory with a land area lesser than 1,000 km² has the highest PM 2.5 level, based on average PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the maximum PM2.5 level, according to its average PM2.5 level?",Chandigarh 295,371,area_based,"Which union territory with a land area greater than 1,000 km² has the 2nd lowest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM10').iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area exceeding 1,000 km² registers the 2nd minimum PM10 level, according to its average PM10 level?",Delhi 296,372,area_based,"Which union territory with a land area greater than 1,000 km² has the highest PM 2.5 level, based on total PM 2.5 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] > 1000] required_state = filtered_data.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which union territory with a land area greater than 1,000 km² shows the highest PM2.5 level, based on its total PM2.5 level?",Delhi 297,373,area_based,"Which union territory with a land area lesser than 1,000 km² has the 2nd highest PM 10 level, based on average PM 10 level?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged_data = pd.merge(state_pm, filtered_data, on='state') filtered_data = merged_data[merged_data['area (km2)'] < 1000] required_state = filtered_data.sort_values('PM10', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Which union territory having a land area less than 1,000 km² registers the 2nd maximum PM10 level, according to its average PM10 level?",Puducherry 298,375,funding_based,In which financial year was the 75th percentile of NCAP funding release the highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.quantile(0.75) avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the 75th percentile of NCAP funding release reach its peak across cities?,2019-20 299,376,funding_based,In which financial year was the median NCAP funding release the highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.median() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the median NCAP funding release the highest among cities?,2020-21 300,377,funding_based,In which financial year was the 25th percentile of NCAP funding release the 2nd highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.quantile(0.25) avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the 25th percentile of NCAP funding release rank 2nd highest across cities?,2021-22 301,378,funding_based,In which financial year was the variance of NCAP funding release the lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.var() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[0]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the variance in NCAP funding release the smallest among cities?,2020-21 302,379,funding_based,In which financial year was the variance of NCAP funding release the 3rd highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.var() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-3]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the variance of NCAP funding release rank 3rd highest across cities?,2020-21 303,380,funding_based,In which financial year was the 25th percentile of NCAP funding release the 2nd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.quantile(0.25) avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[1]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the 25th percentile of NCAP funding release the 2nd lowest among cities?,2021-22 304,381,funding_based,In which financial year was the median NCAP funding release the 3rd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.median() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[2]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the median NCAP funding release rank 3rd lowest across cities?,2020-21 305,382,funding_based,In which financial year was the total NCAP funding release the 3rd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.sum() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[2]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the total NCAP funding release the 3rd smallest among cities?,2019-20 306,384,funding_based,In which financial year was the 75th percentile of NCAP funding release the 2nd highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.quantile(0.75) avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-2]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the 75th percentile of NCAP funding release the 2nd highest among cities?,2020-21 307,386,funding_based,In which financial year was the standard deviation of NCAP funding release the 2nd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.std() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[1]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the standard deviation of NCAP funding release the 2nd smallest among cities?,2021-22 308,387,funding_based,In which financial year was the total NCAP funding release the highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.sum() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-1]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the total NCAP funding release reach its maximum across cities?,2019-20 309,388,funding_based,In which financial year was the median NCAP funding release the 2nd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.median() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[1]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the median NCAP funding release the 2nd lowest among cities?,2021-22 310,390,funding_based,In which financial year was the total NCAP funding release the 2nd lowest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.sum() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[1]['Year'].split()[-1] print(required_year) true_code() ",During which financial year was the total NCAP funding release the 2nd smallest among cities?,2020-21 311,391,funding_based,In which financial year was the median NCAP funding release the 3rd highest across cities?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") df = ncap_funding_data[ ['Amount released during FY 2019-20', 'Amount released during FY 2020-21', 'Amount released during FY 2021-22'] ] avg_series = df.median() avg_series = avg_series.sort_values().reset_index() avg_series.columns = ['Year', 'Amount'] required_year = avg_series.iloc[len(avg_series)-3]['Year'].split()[-1] print(required_year) true_code() ",In which financial year did the median NCAP funding release rank 3rd highest across cities?,2019-20 312,392,funding_based,Report the state(excluding union territories) that received the 5th lowest NCAP funding relative to its land area on a per-square.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged = pd.merge(funding_per_state, filtered_states_data, on='state') merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)'] required_state = merged.sort_values('funding_per_sqkm').iloc[4]['state'] print(required_state) true_code() ",Provide the state (excluding union territories) that obtained the 5th minimum NCAP funding in proportion to its land area per square unit.,Karnataka 313,394,funding_based,Report the state(excluding union territories) that received the 3rd highest NCAP funding relative to its land area on a per-square.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged = pd.merge(funding_per_state, filtered_states_data, on='state') merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)'] required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Provide the state (excluding union territories) that obtained the 3rd highest NCAP funding in proportion to its land area per square unit.,Uttar Pradesh 314,395,funding_based,Report the state(excluding union territories) that received the lowest NCAP funding relative to its land area on a per-square.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'area (km2)']] merged = pd.merge(funding_per_state, filtered_states_data, on='state') merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)'] required_state = merged.sort_values('funding_per_sqkm').iloc[0]['state'] print(required_state) true_code() ",Report the state (excluding union territories) that received the minimum NCAP funding relative to its land area on a per-square basis.,Tamil Nadu 315,397,funding_based,Report the union territory that received the highest NCAP funding relative to its land area on a per-square.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") funding_per_state = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'area (km2)']] merged = pd.merge(funding_per_state, filtered_states_data, on='state') merged['funding_per_sqkm'] = merged['Total fund released'] / merged['area (km2)'] required_state = merged.sort_values('funding_per_sqkm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Report the union territory that received the maximum NCAP funding relative to its land area on a per-square basis.,Chandigarh 316,400,funding_based,Which city has the 5th lowest difference between allocated NCAP funding and actual utilisation as on June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference').iloc[4]['city'] print(req_loc) true_code() ",Which city shows the 5th smallest difference between allocated NCAP funding and its actual use by June 2022?,Raebareli 317,401,funding_based,Which city has the 4th highest difference between allocated NCAP funding and actual utilisation as on June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference', ascending=False).iloc[3]['city'] print(req_loc) true_code() ",Identify the city that has the 4th largest disparity between NCAP funds allocated and those utilized as of June 2022.,Guwahati 318,402,funding_based,Which city has the lowest difference between allocated NCAP funding and actual utilisation as on June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference').iloc[0]['city'] print(req_loc) true_code() ",Determine the city with the minimum difference between its allocated NCAP funding and the amount actually utilized by June 2022.,Aurangabad 319,403,funding_based,Which state has the 3rd lowest difference between allocated NCAP funding and actual utilisation as on June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('state')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference').iloc[2]['state'] print(req_loc) true_code() ",Which state exhibits the 3rd lowest gap between allocated NCAP funding and actual utilization figures as of June 2022?,Andhra Pradesh 320,405,funding_based,"Which city has the 3rd highest, difference between allocated NCAP funding and actual utilisation as on June 2022?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference', ascending=False).iloc[2]['city'] print(req_loc) true_code() ",Identify the city showing the 3rd highest difference between its allocated NCAP funding and the actual utilization by June 2022.,Chandigarh 321,406,funding_based,Which city has the highest difference between allocated NCAP funding and actual utilisation as on June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data['Difference'] = ncap_funding_data['Total fund released'] - ncap_funding_data['Utilisation as on June 2022'] df = ncap_funding_data.groupby('city')['Difference'].sum().reset_index() req_loc = df.sort_values('Difference', ascending=False).iloc[0]['city'] print(req_loc) true_code() ",Determine which city has the maximum disparity between allocated NCAP funding and actual utilization as of June 2022.,Srinagar 322,408,funding_based,Which city saw the highest increment in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[len(sorted_change)-1]['city'] print(result) true_code()",Identify the city that experienced the largest increase in funding between FY 2019-20 and FY 2020-21.,Rishikesh 323,409,funding_based,Which city saw the 2nd lowest decrement in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[1]['city'] print(result) true_code()",Report the city that saw the second smallest reduction in funding from FY 2019-20 to FY 2020-21.,Muzaffarpur 324,410,funding_based,Which city saw the 5th highest increment in funding between FY 2019-20 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[len(sorted_change)-5]['city'] print(result) true_code()",Determine which city witnessed the 5th highest rise in funding between FY 2019-20 and FY 2021-22.,Muzaffarpur 325,411,funding_based,Which city saw the 2nd lowest decrement in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[1]['city'] print(result) true_code()",Which city showed the 2nd least decrease in funding between FY 2020-21 and FY 2021-22?,Hubli-Dharwad 326,412,funding_based,Which city saw the 4th highest decrement in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-4]['city'] print(result) true_code()",Identify the city with the 4th largest drop in funding from FY 2019-20 to FY 2020-21.,Visakhapatnam 327,414,funding_based,Which city saw the 4th highest decrement in funding between FY 2019-20 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-4]['city'] print(result) true_code()",Determine which city experienced the 4th highest decrease in funding between FY 2019-20 and FY 2021-22.,Visakhapatnam 328,415,funding_based,Which city saw the 3rd lowest increment in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[2]['city'] print(result) true_code()",Which city had the 3rd smallest increase in funding from FY 2020-21 to FY 2021-22?,Baddi (Baddi&nalagarh considered twin during FY 20-21) 329,417,funding_based,Which city saw the 5th highest decrement in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-5]['city'] print(result) true_code()",Report the city with the 5th most significant reduction in funding between FY 2020-21 and FY 2021-22.,Muzaffarpur 330,418,funding_based,Which state saw the 2nd lowest increment in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[1]['state'] print(result) true_code()",Determine which state showed the 2nd smallest increment in funding from FY 2020-21 to FY 2021-22.,Andhra Pradesh 331,419,funding_based,Which state saw the 3rd lowest increment in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[2]['state'] print(result) true_code()",Which state witnessed the 3rd least increase in funding between FY 2020-21 and FY 2021-22?,Nagaland 332,421,funding_based,Which state saw the highest decrement in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-1]['state'] print(result) true_code()",Report the state with the most substantial drop in funding between FY 2019-20 and FY 2020-21.,Chandigarh 333,422,funding_based,Which city saw the 4th highest increment in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[len(sorted_change)-4]['city'] print(result) true_code()",Determine which city observed the 4th largest increase in funding between FY 2020-21 and FY 2021-22.,Ujjain 334,423,funding_based,Which city saw the 5th highest decrement in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-5]['city'] print(result) true_code()",Which city saw the 5th highest reduction in funding from FY 2019-20 to FY 2020-21?,Twin City Bhubaneshwar & Cuttack 335,424,funding_based,Which city saw the 3rd lowest increment in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[2]['city'] print(result) true_code()",Identify the city with the 3rd smallest rise in funding between FY 2019-20 and FY 2020-21.,Allahabad 336,425,funding_based,Which state saw the 2nd lowest increment in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[1]['state'] print(result) true_code()",Report the state that had the second least increment in funding between FY 2019-20 and FY 2020-21.,Delhi 337,426,funding_based,Which city saw the highest increment in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[len(sorted_change)-1]['city'] print(result) true_code()",Determine which city experienced the highest increase in funding from FY 2020-21 to FY 2021-22.,Solapur 338,427,funding_based,Which city saw the 2nd lowest increment in funding between FY 2019-20 and FY 2020-21?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2020-21' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=True) result = sorted_change.iloc[1]['city'] print(result) true_code()",Which city showed the 2nd smallest rise in funding between FY 2019-20 and FY 2020-21?,Agra 339,428,funding_based,Which state saw the highest decrement in funding between FY 2020-21 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2020-21' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('state')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[len(sorted_change)-1]['state'] print(result) true_code()",Identify the state that witnessed the largest decrease in funding from FY 2020-21 to FY 2021-22.,Himachal Pradesh 340,432,funding_based,Which city saw the 2nd lowest decrement in funding between FY 2019-20 and FY 2021-22?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") col_start = 'Amount released during FY 2019-20' col_end = 'Amount released during FY 2021-22' ncap_funding_data['change'] = ncap_funding_data[col_end] - ncap_funding_data[col_start] funding_change = ncap_funding_data.groupby('city')['change'].sum().reset_index() sorted_change = funding_change.sort_values('change', ascending=False) result = sorted_change.iloc[1]['city'] print(result) true_code()",Identify the city that experienced the second smallest reduction in funding between FY 2019-20 and FY 2021-22.,Solapur 341,433,funding_based,Which state received the lowest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released').iloc[0]['state'] print(required_state) true_code() ",Which state was allocated the smallest amount of NCAP funding?,Meghalaya 342,434,funding_based,Which city received the 5th lowest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released').iloc[4]['city'] print(required_state) true_code() ",Report the city that obtained the 5th lowest NCAP funding.,Nashik 343,435,funding_based,Which city received the 4th highest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released', ascending=False).iloc[3]['city'] print(required_state) true_code() ",Identify the city which received the 4th highest NCAP funding amount.,Hyderabad 344,436,funding_based,Which city received the lowest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released').iloc[0]['city'] print(required_state) true_code() ",Determine the city allocated the least NCAP funding.,Visakhapatnam 345,437,funding_based,Which state received the 3rd lowest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released').iloc[2]['state'] print(required_state) true_code() ",Which state received the 3rd smallest NCAP funding?,Jharkhand 346,438,funding_based,Which city received the 2nd lowest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released').iloc[1]['city'] print(required_state) true_code() ",Report the city that was granted the 2nd lowest NCAP funding.,Nalagarh 347,439,funding_based,"Which city received the 3rd highest, NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released', ascending=False).iloc[2]['city'] print(required_state) true_code() ",Identify the city which obtained the 3rd highest NCAP funding.,Delhi 348,440,funding_based,Which city received the highest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('city')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released', ascending=False).iloc[0]['city'] print(required_state) true_code() ",Determine the city that received the largest NCAP funding amount.,Chandigarh 349,441,funding_based,Which state received the 5th highest NCAP funding?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() required_state = state_funding.sort_values('Total fund released', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Which state was allocated the 5th highest NCAP funding?,Madhya Pradesh 350,442,funding_based,Which state utilised the lowest percentage of its allocated NCAP funding as of June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data = ncap_funding_data.groupby('state')[['Total fund released','Utilisation as on June 2022']].sum().reset_index() ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / ncap_funding_data['Total fund released']) * 100 ans = ncap_funding_data.sort_values('utilisation_percent').iloc[0]['state'] print(ans) true_code() ",Which state showed the lowest percentage utilization of its allocated NCAP funds as of June 2022?,Delhi 351,443,funding_based,Which city utilised the 5th lowest percentage of its allocated NCAP funding as of June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index() ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / ncap_funding_data['Total fund released']) * 100 ans = ncap_funding_data.sort_values('utilisation_percent').iloc[4]['city'] print(ans) true_code() ",Report the city that utilized the 5th smallest percentage of its allocated NCAP funding by June 2022.,Guwahati 352,445,funding_based,Which city utilised the lowest percentage of its allocated NCAP funding as of June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index() ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / ncap_funding_data['Total fund released']) * 100 ans = ncap_funding_data.sort_values('utilisation_percent').iloc[0]['city'] print(ans) true_code() ",Determine the city that utilized the minimum percentage of its allocated NCAP funding by June 2022.,Delhi 353,448,funding_based,Which city utilised the 3rd highest percentage of its allocated NCAP funding as of June 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") ncap_funding_data = ncap_funding_data.groupby('city')[['Total fund released','Utilisation as on June 2022']].sum().reset_index() ncap_funding_data['utilisation_percent'] = (ncap_funding_data['Utilisation as on June 2022'] / ncap_funding_data['Total fund released']) * 100 ans = ncap_funding_data.sort_values('utilisation_percent', ascending=False).iloc[2]['city'] print(ans) true_code() ",Identify the city with the 3rd highest percentage utilization of its allocated NCAP funds as of June 2022.,Aurangabad 354,454,funding_based,Identify the state that has the lowest number of cities receiving NCAP funding.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index() max_cities_state = state_city_counts.sort_values('city').iloc[0]['state'] print(max_cities_state) true_code() ",Determine the state with the smallest count of cities receiving NCAP funding.,Chandigarh 355,455,funding_based,Identify the state that has the 4th highest number of cities receiving NCAP funding.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_city_counts = ncap_funding_data.groupby('state')['city'].nunique().reset_index() max_cities_state = state_city_counts.sort_values('city', ascending=False).iloc[3]['state'] print(max_cities_state) true_code() ",Which state features the 4th largest number of cities under the NCAP funding scheme?,Punjab 356,457,funding_based,Identify the union territory that has the 2nd highest number of cities receiving NCAP funding.," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") city_count = ncap_funding_data.groupby('state')['city'].nunique().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = pd.merge(filtered_states_data, city_count, on='state', how='inner') max_cities_state = merged_df.sort_values('city', ascending=False).iloc[1]['state'] print(max_cities_state) true_code() ",Report the union territory that has the 2nd most cities benefiting from NCAP funding.,Delhi 357,458,funding_based,Which city with NCAP funding has the 3rd lowest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10').iloc[2]['city'] print(ans) true_code() ",Which city with NCAP funding shows the 3rd lowest PM10 concentration?,Rishikesh 358,459,funding_based,Which state with NCAP funding has the 3rd lowest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5').iloc[2]['state'] print(ans) true_code() ",Determine the state with NCAP funding that has the 3rd lowest PM2.5 levels.,Chhattisgarh 359,460,funding_based,Which city with NCAP funding has the 2nd highest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[1]['city'] print(ans) true_code() ",Identify the city receiving NCAP funding with the 2nd highest PM10 concentration.,Delhi 360,461,funding_based,Which city with NCAP funding has the 4th lowest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5').iloc[3]['city'] print(ans) true_code() ",Report the city with NCAP funding that exhibits the 4th lowest PM2.5 levels.,Srinagar 361,462,funding_based,Which state with NCAP funding has the highest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5', ascending=False).iloc[0]['state'] print(ans) true_code() ",Which state with NCAP funding records the highest PM2.5 concentration?,Delhi 362,463,funding_based,Which state with NCAP funding has the 2nd highest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5', ascending=False).iloc[1]['state'] print(ans) true_code() ",Determine the state receiving NCAP funding that has the 2nd highest PM2.5 levels.,Bihar 363,465,funding_based,Which city with NCAP funding has the 4th lowest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10').iloc[3]['city'] print(ans) true_code() ",Report the city with NCAP funding that has the 4th lowest PM10 levels.,Anantapur 364,466,funding_based,Which city with NCAP funding has the 5th highest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[4]['city'] print(ans) true_code() ",Which city receiving NCAP funding exhibits the 5th highest PM10 concentration?,Muzaffarpur 365,468,funding_based,Which state with NCAP funding has the 4th lowest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10').iloc[3]['state'] print(ans) true_code() ",Identify the state with NCAP funding having the 4th lowest PM10 concentration.,Nagaland 366,469,funding_based,Which state with NCAP funding has the 2nd lowest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10').iloc[1]['state'] print(ans) true_code() ",Report the state receiving NCAP funding that shows the 2nd lowest PM10 levels.,Tamil Nadu 367,470,funding_based,Which state with NCAP funding has the 2nd highest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[1]['state'] print(ans) true_code() ",Which state with NCAP funding exhibits the 2nd highest PM10 concentration?,Bihar 368,471,funding_based,Which city with NCAP funding has the 4th highest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10', ascending=False).iloc[3]['city'] print(ans) true_code() ",Determine the city with NCAP funding that has the 4th highest PM10 levels.,Patna 369,472,funding_based,Which city with NCAP funding has the 5th lowest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('city')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['city'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['city'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5').iloc[4]['city'] print(ans) true_code() ",Identify the city receiving NCAP funding showing the 5th lowest PM2.5 levels.,Rishikesh 370,473,funding_based,Which state with NCAP funding has the 2nd lowest PM 2.5 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM2.5').iloc[1]['state'] print(ans) true_code() ",Report the state with NCAP funding that records the 2nd lowest PM2.5 concentration.,Jammu and Kashmir 371,474,funding_based,Which state with NCAP funding has the 3rd lowest PM 10 levels?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm25_avg = main_data.groupby('state')['PM10'].mean().reset_index() funded_states = ncap_funding_data[ncap_funding_data['Total fund released'] > 0]['state'].unique() funded_pm_states = state_pm25_avg[state_pm25_avg['state'].isin(funded_states)] ans = funded_pm_states.sort_values('PM10').iloc[2]['state'] print(ans) true_code() ",Which state receiving NCAP funding has the 3rd lowest PM10 levels?,Karnataka 372,476,funding_based,Which state has the 5th highest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state having the 5th highest NCAP funding considering its median PM2.5 concentration in 2022 (FY 2021-22).,Uttarakhand 373,477,funding_based,Which city has the highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city received the highest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20).,Nagpur 374,478,funding_based,Which city has the highest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Which city got the highest NCAP funding relative to its total PM10 concentration in 2022 (FY 2021-22)?,Akola 375,479,funding_based,Which state has the 2nd highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state with the 2nd highest NCAP funding considering the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Chandigarh 376,480,funding_based,Which state has the 5th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state that received the 5th lowest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Odisha 377,481,funding_based,Which city has the 5th highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Determine the city having the 5th highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Jalandhar 378,482,funding_based,Which state has the 2nd lowest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd lowest NCAP funding considering the variance of its PM2.5 concentration in 2021 (FY 2020-21)?,Uttar Pradesh 379,483,funding_based,Which state has the 4th lowest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state with the 4th lowest NCAP funding with respect to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Uttar Pradesh 380,484,funding_based,Which city has the 5th highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city that received the 5th highest NCAP funding relative to its total PM10 concentration in 2021 (FY 2020-21).,Badlapur 381,485,funding_based,Which city has the 3rd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city had the 3rd highest NCAP funding considering its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,Guwahati 382,486,funding_based,Which city has the 5th highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city was granted the 5th highest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20)?,Mumbai 383,488,funding_based,Which state has the 2nd lowest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state with the 2nd lowest NCAP funding considering its total PM10 concentration in 2020 (FY 2019-20).,Uttar Pradesh 384,489,funding_based,Which state has the 2nd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Determine which state got the 2nd highest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 385,492,funding_based,Which state has the 4th lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state granted the 4th lowest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Assam 386,493,funding_based,Which city has the 2nd highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city had the 2nd highest NCAP funding relative to its average PM2.5 concentration in 2020 (FY 2019-20).,Hyderabad 387,494,funding_based,Which state has the 4th highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th highest NCAP funding considering the variance of its PM10 concentration in 2021 (FY 2020-21)?,Chhattisgarh 388,495,funding_based,Which city has the 2nd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city with the 2nd highest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Gorakhpur 389,496,funding_based,Which state has the 4th highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state that got the 4th highest NCAP funding relative to its average PM2.5 concentration in 2020 (FY 2019-20).,Madhya Pradesh 390,497,funding_based,Which state has the 4th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Determine which state was granted the 4th highest NCAP funding considering the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Madhya Pradesh 391,499,funding_based,Which city has the 4th lowest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th lowest NCAP funding relative to its total PM2.5 concentration in 2022 (FY 2021-22).,Gaya 392,500,funding_based,Which city has the 5th highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th highest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Mumbai 393,501,funding_based,Which state has the 3rd lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Determine the state which got the 3rd lowest NCAP funding with respect to its total PM2.5 concentration in 2020 (FY 2019-20).,Uttar Pradesh 394,502,funding_based,Which state has the lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Which state received the lowest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21)?,Madhya Pradesh 395,503,funding_based,Which city has the 3rd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city with the 3rd lowest NCAP funding considering its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Khanna 396,504,funding_based,Which city has the lowest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the lowest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21).,Ujjain 397,507,funding_based,Which city has the 2nd lowest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding with respect to its average PM2.5 concentration in 2022 (FY 2021-22).,Talcher 398,508,funding_based,Which city has the 2nd lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city with the 2nd lowest NCAP funding relative to its total PM2.5 concentration in 2020 (FY 2019-20).,Khanna 399,509,funding_based,Which state has the 5th highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 5th highest NCAP funding considering its average PM10 concentration in 2021 (FY 2020-21).,Assam 400,510,funding_based,Which city has the 5th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Which city had the 5th lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21)?,Sagar 401,512,funding_based,Which state has the highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Report the state with the highest NCAP funding considering its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Meghalaya 402,513,funding_based,Which city has the 5th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Determine which city got the 5th lowest NCAP funding with respect to the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Udaipur 403,515,funding_based,Which city has the 2nd highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city with the 2nd highest NCAP funding considering its median PM10 concentration in 2022 (FY 2021-22).,Srinagar 404,516,funding_based,Which city has the 2nd highest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 2nd highest NCAP funding with respect to its median PM2.5 concentration in 2022 (FY 2021-22).,Gorakhpur 405,517,funding_based,Which city has the 2nd lowest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city had the 2nd lowest NCAP funding relative to its total PM10 concentration in 2020 (FY 2019-20).,Patiala 406,518,funding_based,Which state has the 3rd lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Which state got the 3rd lowest NCAP funding considering the standard deviation of its PM10 concentration in 2021 (FY 2020-21)?,Punjab 407,519,funding_based,Which city has the 2nd lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Talcher 408,520,funding_based,Which state has the 4th highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state with the 4th highest NCAP funding relative to its average PM10 concentration in 2021 (FY 2020-21).,Tamil Nadu 409,521,funding_based,Which city has the lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the lowest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Alwar 410,523,funding_based,Which state has the 2nd lowest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 2nd lowest NCAP funding relative to its median PM10 concentration in 2022 (FY 2021-22).,Meghalaya 411,524,funding_based,Which state has the 5th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th highest NCAP funding considering the standard deviation of its PM10 concentration in 2021 (FY 2020-21).,Nagaland 412,525,funding_based,Which city has the lowest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city got the lowest NCAP funding with respect to its median PM2.5 concentration in 2021 (FY 2020-21).,Ujjain 413,526,funding_based,Which city has the 3rd highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Which city received the 3rd highest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22)?,Dehradun 414,527,funding_based,Which city has the 3rd lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city with the 3rd lowest NCAP funding considering its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Kohima 415,529,funding_based,Which state has the 5th lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Determine which state had the 5th lowest NCAP funding relative to its total PM2.5 concentration in 2021 (FY 2020-21).,Uttar Pradesh 416,530,funding_based,Which city has the 2nd highest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Which city got the 2nd highest NCAP funding considering the standard deviation of its PM10 concentration in 2022 (FY 2021-22)?,Gorakhpur 417,532,funding_based,Which city has the 4th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th highest NCAP funding relative to the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Pune 418,533,funding_based,Which state has the 2nd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 2nd lowest NCAP funding considering its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,Madhya Pradesh 419,534,funding_based,Which state has the 5th highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Which state had the 5th highest NCAP funding with respect to the variance of its PM10 concentration in 2021 (FY 2020-21)?,Jammu and Kashmir 420,535,funding_based,Which city has the 2nd lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Udaipur 421,536,funding_based,Which city has the 5th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th highest NCAP funding considering the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Delhi 422,537,funding_based,Which city has the 5th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Determine which city got the 5th lowest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Anantapur 423,538,funding_based,Which state has the highest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Which state received the highest NCAP funding relative to its average PM10 concentration in 2022 (FY 2021-22)?,Jammu and Kashmir 424,539,funding_based,Which city has the 5th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city with the 5th highest NCAP funding considering the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,Delhi 425,540,funding_based,Which state has the 2nd highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 2nd highest NCAP funding with respect to its median PM2.5 concentration in 2021 (FY 2020-21).,Jammu and Kashmir 426,541,funding_based,Which state has the 3rd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Determine which state had the 3rd lowest NCAP funding relative to its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Assam 427,542,funding_based,Which state has the highest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Which state got the highest NCAP funding considering its total PM2.5 concentration in 2021 (FY 2020-21)?,Uttarakhand 428,543,funding_based,Which city has the lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city that received the lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,Ujjain 429,544,funding_based,Which city has the 3rd highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Report the city with the 3rd highest NCAP funding relative to the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Chandigarh 430,545,funding_based,Which city has the 4th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 4th lowest NCAP funding considering its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Dewas 431,548,funding_based,Which state has the highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Report the state with the highest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Telangana 432,549,funding_based,Which state has the 5th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Determine which state got the 5th lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 433,550,funding_based,Which city has the 2nd highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Which city received the 2nd highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22)?,Gorakhpur 434,551,funding_based,Which state has the lowest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Identify the state with the lowest NCAP funding considering its total PM10 concentration in 2022 (FY 2021-22).,Himachal Pradesh 435,552,funding_based,Which city has the 4th highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th highest NCAP funding with respect to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Jalandhar 436,553,funding_based,Which state has the 4th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Determine which state had the 4th highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Madhya Pradesh 437,554,funding_based,Which city has the 5th highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city got the 5th highest NCAP funding considering its 25th percentile of PM10 concentration in 2022 (FY 2021-22)?,Chandigarh 438,555,funding_based,Which state has the highest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Identify the state that received the highest NCAP funding with respect to its median PM10 concentration in 2020 (FY 2019-20).,Telangana 439,556,funding_based,Which city has the highest NCAP funding with respect to average PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city with the highest NCAP funding relative to its average PM10 concentration in 2020 (FY 2019-20).,Nagpur 440,557,funding_based,Which state has the 3rd lowest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 3rd lowest NCAP funding considering its average PM10 concentration in 2022 (FY 2021-22).,Himachal Pradesh 441,558,funding_based,Which state has the 4th lowest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state had the 4th lowest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21)?,Uttar Pradesh 442,559,funding_based,Which city has the 4th highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th highest NCAP funding relative to its median PM2.5 concentration in 2021 (FY 2020-21).,Kohima 443,560,funding_based,Which city has the 4th highest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th highest NCAP funding considering its average PM10 concentration in 2022 (FY 2021-22).,Delhi 444,561,funding_based,Which state has the 3rd highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Determine which state got the 3rd highest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21).,Himachal Pradesh 445,562,funding_based,Which state has the 4th lowest NCAP funding with respect to median PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th lowest NCAP funding relative to its median PM10 concentration in 2021 (FY 2020-21)?,Telangana 446,563,funding_based,Which state has the 2nd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state with the 2nd highest NCAP funding considering its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Chandigarh 447,564,funding_based,Which state has the 2nd lowest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 2nd lowest NCAP funding with respect to the variance of its PM2.5 concentration in 2020 (FY 2019-20).,Uttar Pradesh 448,565,funding_based,Which state has the 2nd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Determine which state had the 2nd lowest NCAP funding relative to its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Rajasthan 449,566,funding_based,Which state has the 2nd lowest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd lowest NCAP funding considering its median PM2.5 concentration in 2022 (FY 2021-22)?,Meghalaya 450,567,funding_based,Which city has the 5th highest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 5th highest NCAP funding with respect to its average PM2.5 concentration in 2021 (FY 2020-21).,Guwahati 451,568,funding_based,Which city has the 2nd highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city with the 2nd highest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Srinagar 452,570,funding_based,Which state has the 5th lowest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Which state had the 5th lowest NCAP funding with respect to its average PM2.5 concentration in 2022 (FY 2021-22)?,Himachal Pradesh 453,571,funding_based,Which state has the 4th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 4th lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 454,572,funding_based,Which state has the lowest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Report the state with the lowest NCAP funding considering its average PM10 concentration in 2021 (FY 2020-21).,Punjab 455,574,funding_based,Which city has the 3rd highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Which city received the 3rd highest NCAP funding relative to the variance of its PM10 concentration in 2021 (FY 2020-21)?,Srinagar 456,575,funding_based,Which city has the 4th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city with the 4th lowest NCAP funding considering the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Gaya 457,576,funding_based,Which city has the 5th highest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 5th highest NCAP funding with respect to its median PM10 concentration in 2020 (FY 2019-20).,Mumbai 458,577,funding_based,Which city has the 2nd lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city had the 2nd lowest NCAP funding relative to its total PM2.5 concentration in 2021 (FY 2020-21).,Dewas 459,578,funding_based,Which city has the 5th lowest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Which city got the 5th lowest NCAP funding considering its average PM2.5 concentration in 2022 (FY 2021-22)?,Muzaffarpur 460,579,funding_based,Which city has the 2nd lowest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding with respect to the variance of its PM10 concentration in 2022 (FY 2021-22).,Talcher 461,580,funding_based,Which city has the 4th lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th lowest NCAP funding relative to its 75th percentile of PM10 concentration in 2022 (FY 2021-22).,Gaya 462,581,funding_based,Which state has the lowest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the lowest NCAP funding considering its median PM10 concentration in 2020 (FY 2019-20).,Odisha 463,583,funding_based,Which state has the 5th lowest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 5th lowest NCAP funding relative to its median PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 464,585,funding_based,Which city has the 2nd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city got the 2nd lowest NCAP funding with respect to its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,Dewas 465,586,funding_based,Which city has the highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Which city received the highest NCAP funding relative to its 75th percentile of PM10 concentration in 2022 (FY 2021-22)?,Gorakhpur 466,587,funding_based,Which state has the 2nd highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state with the 2nd highest NCAP funding considering the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Chandigarh 467,588,funding_based,Which city has the 5th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 5th lowest NCAP funding with respect to the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,Muzaffarpur 468,589,funding_based,Which state has the 2nd lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Determine which state had the 2nd lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Rajasthan 469,590,funding_based,Which state has the 4th lowest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state got the 4th lowest NCAP funding considering its average PM2.5 concentration in 2021 (FY 2020-21)?,Maharashtra 470,591,funding_based,Which city has the 3rd lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 3rd lowest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Kohima 471,592,funding_based,Which city has the 5th lowest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th lowest NCAP funding relative to the variance of its PM10 concentration in 2022 (FY 2021-22).,Kohima 472,593,funding_based,Which city has the 3rd highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 3rd highest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,Muzaffarpur 473,595,funding_based,Which city has the 4th lowest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th lowest NCAP funding relative to its average PM2.5 concentration in 2021 (FY 2020-21).,Moradabad 474,597,funding_based,Which state has the 2nd highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Determine the state which got the 2nd highest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20).,Chandigarh 475,598,funding_based,Which city has the lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Which city received the lowest NCAP funding relative to its total PM2.5 concentration in 2020 (FY 2019-20)?,Muzaffarpur 476,599,funding_based,Which state has the 4th lowest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state with the 4th lowest NCAP funding considering its average PM2.5 concentration in 2020 (FY 2019-20).,Assam 477,600,funding_based,Which city has the 2nd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 2nd highest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Hyderabad 478,601,funding_based,Which state has the lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state had the lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Punjab 479,602,funding_based,Which city has the 5th highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city got the 5th highest NCAP funding considering its average PM10 concentration in 2021 (FY 2020-21)?,Kohima 480,603,funding_based,Which city has the 5th lowest NCAP funding with respect to average PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 5th lowest NCAP funding with respect to its average PM10 concentration in 2020 (FY 2019-20).,Alwar 481,604,funding_based,Which state has the 4th highest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state with the 4th highest NCAP funding relative to its average PM10 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 482,606,funding_based,Which city has the 3rd lowest NCAP funding with respect to median PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Which city had the 3rd lowest NCAP funding with respect to its median PM2.5 concentration in 2020 (FY 2019-20)?,Patiala 483,607,funding_based,Which city has the 5th lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 5th lowest NCAP funding relative to its total PM2.5 concentration in 2021 (FY 2020-21).,Gaya 484,608,funding_based,Which state has the 5th lowest NCAP funding with respect to variance of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th lowest NCAP funding considering the variance of its PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 485,609,funding_based,Which state has the highest NCAP funding with respect to average PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state got the highest NCAP funding with respect to its average PM10 concentration in 2020 (FY 2019-20).,Telangana 486,610,funding_based,Which state has the lowest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Which state received the lowest NCAP funding relative to the variance of its PM2.5 concentration in 2021 (FY 2020-21)?,Uttar Pradesh 487,611,funding_based,Which state has the 2nd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state with the 2nd lowest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,Madhya Pradesh 488,612,funding_based,Which state has the 3rd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 3rd lowest NCAP funding with respect to its 25th percentile of PM10 concentration in 2022 (FY 2021-22).,Himachal Pradesh 489,614,funding_based,Which state has the 2nd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd lowest NCAP funding considering its 75th percentile of PM10 concentration in 2020 (FY 2019-20)?,Assam 490,615,funding_based,Which city has the 2nd highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd highest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21).,Amravati 491,616,funding_based,Which city has the 4th lowest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th lowest NCAP funding relative to the variance of its PM10 concentration in 2021 (FY 2020-21).,Sagar 492,617,funding_based,Which state has the 5th lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 5th lowest NCAP funding considering its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,Maharashtra 493,618,funding_based,Which state has the 4th highest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Which state had the 4th highest NCAP funding with respect to its total PM2.5 concentration in 2020 (FY 2019-20)?,Chhattisgarh 494,619,funding_based,Which city has the 4th lowest NCAP funding with respect to variance of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th lowest NCAP funding relative to the variance of its PM2.5 concentration in 2022 (FY 2021-22).,Gaya 495,620,funding_based,Which state has the highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Report the state with the highest NCAP funding considering its 75th percentile of PM10 concentration in 2021 (FY 2020-21).,Meghalaya 496,621,funding_based,Which city has the 3rd lowest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city got the 3rd lowest NCAP funding with respect to its average PM10 concentration in 2022 (FY 2021-22).,Kohima 497,622,funding_based,Which city has the 3rd highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Which city received the 3rd highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21)?,Jalandhar 498,623,funding_based,Which state has the 2nd lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state with the 2nd lowest NCAP funding considering its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Maharashtra 499,624,funding_based,Which city has the 4th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th highest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Bhopal 500,625,funding_based,Which city has the 5th lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Determine which city had the 5th lowest NCAP funding relative to its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Kohima 501,626,funding_based,Which state has the 4th highest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Which state got the 4th highest NCAP funding considering its total PM10 concentration in 2022 (FY 2021-22)?,Uttarakhand 502,628,funding_based,Which state has the 5th lowest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th lowest NCAP funding relative to its total PM10 concentration in 2022 (FY 2021-22).,Bihar 503,629,funding_based,Which city has the lowest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the lowest NCAP funding considering the variance of its PM10 concentration in 2021 (FY 2020-21).,Ujjain 504,630,funding_based,Which state has the 2nd highest NCAP funding with respect to variance of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Which state had the 2nd highest NCAP funding with respect to the variance of its PM10 concentration in 2020 (FY 2019-20)?,Chandigarh 505,631,funding_based,Which state has the 2nd lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 2nd lowest NCAP funding relative to its total PM2.5 concentration in 2021 (FY 2020-21).,Maharashtra 506,632,funding_based,Which city has the 2nd lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city with the 2nd lowest NCAP funding considering the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,Dewas 507,633,funding_based,Which state has the 3rd highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Determine which state got the 3rd highest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Assam 508,634,funding_based,Which state has the 3rd highest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Which state received the 3rd highest NCAP funding relative to its average PM2.5 concentration in 2021 (FY 2020-21)?,Chandigarh 509,635,funding_based,Which city has the lowest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city with the lowest NCAP funding considering its average PM2.5 concentration in 2020 (FY 2019-20).,Muzaffarpur 510,636,funding_based,Which city has the 4th lowest NCAP funding with respect to median PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th lowest NCAP funding with respect to its median PM2.5 concentration in 2020 (FY 2019-20).,Udaipur 511,637,funding_based,Which city has the 2nd lowest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city had the 2nd lowest NCAP funding relative to its median PM10 concentration in 2022 (FY 2021-22).,Talcher 512,638,funding_based,Which city has the 3rd highest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Which city got the 3rd highest NCAP funding considering its average PM2.5 concentration in 2021 (FY 2020-21)?,Kohima 513,639,funding_based,Which city has the 3rd lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 3rd lowest NCAP funding with respect to the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,Kohima 514,640,funding_based,Which city has the 4th highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th highest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Nagpur 515,641,funding_based,Which city has the 3rd highest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 3rd highest NCAP funding considering its median PM2.5 concentration in 2022 (FY 2021-22).,Dehradun 516,643,funding_based,Which state has the 5th highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 5th highest NCAP funding relative to its median PM2.5 concentration in 2021 (FY 2020-21).,Nagaland 517,644,funding_based,Which city has the highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city with the highest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,Guwahati 518,645,funding_based,Which city has the 2nd lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city got the 2nd lowest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Solapur 519,646,funding_based,Which state has the 4th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th lowest NCAP funding relative to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21)?,Maharashtra 520,647,funding_based,Which city has the 3rd highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city with the 3rd highest NCAP funding considering its median PM2.5 concentration in 2021 (FY 2020-21).,Howrah 521,649,funding_based,Which state has the highest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state had the highest NCAP funding relative to the variance of its PM10 concentration in 2022 (FY 2021-22).,Karnataka 522,650,funding_based,Which city has the 2nd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Which city got the 2nd lowest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20)?,Khanna 523,651,funding_based,Which state has the 3rd highest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 3rd highest NCAP funding with respect to its total PM2.5 concentration in 2020 (FY 2019-20).,Chhattisgarh 524,652,funding_based,Which state has the 4th highest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state with the 4th highest NCAP funding relative to the variance of its PM10 concentration in 2022 (FY 2021-22).,Chandigarh 525,653,funding_based,Which city has the highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the highest NCAP funding considering its 25th percentile of PM10 concentration in 2022 (FY 2021-22).,Gorakhpur 526,655,funding_based,Which state has the 3rd lowest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 3rd lowest NCAP funding relative to its average PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 527,656,funding_based,Which city has the 2nd highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city with the 2nd highest NCAP funding considering the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Alwar 528,657,funding_based,Which city has the highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city got the highest NCAP funding with respect to the variance of its PM10 concentration in 2021 (FY 2020-21).,Chandigarh 529,659,funding_based,Which state has the 3rd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Identify the state with the 3rd lowest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Odisha 530,662,funding_based,Which city has the 5th highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city got the 5th highest NCAP funding considering its median PM2.5 concentration in 2021 (FY 2020-21)?,Guwahati 531,663,funding_based,Which city has the highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city that received the highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Nagpur 532,664,funding_based,Which city has the 5th highest NCAP funding with respect to median PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th highest NCAP funding relative to its median PM10 concentration in 2021 (FY 2020-21).,Kohima 533,665,funding_based,Which city has the 2nd lowest NCAP funding with respect to variance of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 2nd lowest NCAP funding considering the variance of its PM2.5 concentration in 2022 (FY 2021-22).,Talcher 534,666,funding_based,Which city has the 5th lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Which city had the 5th lowest NCAP funding with respect to its 25th percentile of PM10 concentration in 2021 (FY 2020-21)?,Moradabad 535,667,funding_based,Which city has the highest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city that received the highest NCAP funding relative to its median PM2.5 concentration in 2022 (FY 2021-22).,Srinagar 536,669,funding_based,Which city has the lowest NCAP funding with respect to variance of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city got the lowest NCAP funding with respect to the variance of its PM10 concentration in 2022 (FY 2021-22).,Byrnihat 537,670,funding_based,Which state has the 4th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22)?,Uttarakhand 538,671,funding_based,Which state has the 4th highest NCAP funding with respect to variance of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state with the 4th highest NCAP funding considering the variance of its PM10 concentration in 2020 (FY 2019-20).,Madhya Pradesh 539,672,funding_based,Which state has the 2nd highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 2nd highest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 540,674,funding_based,Which state has the 2nd lowest NCAP funding with respect to median PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd lowest NCAP funding considering its median PM2.5 concentration in 2020 (FY 2019-20)?,Odisha 541,675,funding_based,Which city has the 4th highest NCAP funding with respect to average PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th highest NCAP funding with respect to its average PM10 concentration in 2020 (FY 2019-20).,Chandigarh 542,677,funding_based,Which state has the 4th lowest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Determine which state was granted the 4th lowest NCAP funding considering the variance of its PM10 concentration in 2021 (FY 2020-21).,Uttar Pradesh 543,678,funding_based,Which state has the lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Which state had the lowest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22)?,Himachal Pradesh 544,680,funding_based,Which state has the 5th highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th highest NCAP funding considering its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Chandigarh 545,682,funding_based,Which state has the 3rd lowest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Which state received the 3rd lowest NCAP funding relative to its average PM10 concentration in 2021 (FY 2020-21)?,Madhya Pradesh 546,683,funding_based,Which city has the 5th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city with the 5th highest NCAP funding considering the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Mumbai 547,684,funding_based,Which city has the 2nd highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 2nd highest NCAP funding with respect to its average PM10 concentration in 2021 (FY 2020-21).,Chandigarh 548,685,funding_based,Which state has the 5th highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Determine which state had the 5th highest NCAP funding relative to its median PM10 concentration in 2022 (FY 2021-22).,Uttarakhand 549,687,funding_based,Which city has the 2nd highest NCAP funding with respect to median PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd highest NCAP funding with respect to its median PM2.5 concentration in 2020 (FY 2019-20).,Hyderabad 550,688,funding_based,Which city has the 5th lowest NCAP funding with respect to average PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th lowest NCAP funding relative to its average PM10 concentration in 2022 (FY 2021-22).,Gaya 551,689,funding_based,Which state has the 4th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 4th lowest NCAP funding considering the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Assam 552,690,funding_based,Which state has the 3rd highest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Which state had the 3rd highest NCAP funding with respect to the standard deviation of its PM10 concentration in 2021 (FY 2020-21)?,Jammu and Kashmir 553,691,funding_based,Which state has the 4th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 4th lowest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 554,692,funding_based,Which state has the 5th highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th highest NCAP funding considering its total PM10 concentration in 2021 (FY 2020-21).,Himachal Pradesh 555,693,funding_based,Which city has the 3rd highest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city got the 3rd highest NCAP funding with respect to the variance of its PM2.5 concentration in 2020 (FY 2019-20).,Hyderabad 556,694,funding_based,Which state has the 2nd lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state received the 2nd lowest NCAP funding relative to the standard deviation of its PM10 concentration in 2020 (FY 2019-20)?,Assam 557,696,funding_based,Which city has the 3rd lowest NCAP funding with respect to variance of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 3rd lowest NCAP funding with respect to the variance of its PM10 concentration in 2020 (FY 2019-20).,Guwahati 558,697,funding_based,Which state has the highest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state had the highest NCAP funding relative to the variance of its PM2.5 concentration in 2020 (FY 2019-20).,Chandigarh 559,698,funding_based,Which state has the 2nd highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd highest NCAP funding considering its median PM10 concentration in 2022 (FY 2021-22)?,Jammu and Kashmir 560,699,funding_based,Which city has the 2nd lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Patiala 561,700,funding_based,Which state has the 3rd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Report the state with the 3rd highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Chandigarh 562,701,funding_based,Which city has the 2nd highest NCAP funding with respect to variance of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 2nd highest NCAP funding considering the variance of its PM2.5 concentration in 2022 (FY 2021-22).,Srinagar 563,702,funding_based,Which city has the 2nd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Which city had the 2nd highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2022 (FY 2021-22)?,Srinagar 564,703,funding_based,Which state has the 5th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 5th highest NCAP funding relative to the standard deviation of its PM10 concentration in 2020 (FY 2019-20).,Maharashtra 565,704,funding_based,Which city has the 3rd lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Report the city with the 3rd lowest NCAP funding considering its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Udaipur 566,705,funding_based,Which city has the 2nd lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city got the 2nd lowest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Khanna 567,706,funding_based,Which state has the 3rd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Which state received the 3rd highest NCAP funding relative to its 75th percentile of PM10 concentration in 2020 (FY 2019-20)?,Karnataka 568,708,funding_based,Which state has the highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the highest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20).,Telangana 569,709,funding_based,Which state has the 3rd highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Determine which state had the 3rd highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,Chandigarh 570,710,funding_based,Which state has the 4th lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state got the 4th lowest NCAP funding considering its 75th percentile of PM10 concentration in 2020 (FY 2019-20)?,Assam 571,711,funding_based,Which state has the 5th highest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 5th highest NCAP funding with respect to its total PM2.5 concentration in 2020 (FY 2019-20).,Himachal Pradesh 572,712,funding_based,Which state has the 5th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th highest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,Chhattisgarh 573,713,funding_based,Which city has the 4th highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 4th highest NCAP funding considering its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Delhi 574,714,funding_based,Which state has the 3rd lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Which state had the 3rd lowest NCAP funding with respect to its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20)?,Assam 575,715,funding_based,Which state has the 2nd highest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 2nd highest NCAP funding relative to its average PM2.5 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 576,717,funding_based,Which state has the 3rd highest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Determine which state got the 3rd highest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20).,Madhya Pradesh 577,718,funding_based,Which city has the 5th highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city received the 5th highest NCAP funding relative to its 75th percentile of PM10 concentration in 2021 (FY 2020-21)?,Howrah 578,719,funding_based,Which city has the lowest NCAP funding with respect to median PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city with the lowest NCAP funding considering its median PM2.5 concentration in 2020 (FY 2019-20).,Alwar 579,720,funding_based,Which state has the 5th highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 5th highest NCAP funding with respect to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Chandigarh 580,722,funding_based,Which city has the 5th lowest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Which city got the 5th lowest NCAP funding considering its total PM10 concentration in 2020 (FY 2019-20)?,Guwahati 581,723,funding_based,Which state has the highest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Identify the state that received the highest NCAP funding with respect to its median PM10 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 582,724,funding_based,Which state has the 2nd lowest NCAP funding with respect to variance of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state with the 2nd lowest NCAP funding relative to the variance of its PM10 concentration in 2020 (FY 2019-20).,Uttar Pradesh 583,725,funding_based,Which city has the lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the lowest NCAP funding considering its 25th percentile of PM10 concentration in 2022 (FY 2021-22).,Byrnihat 584,726,funding_based,Which city has the 4th lowest NCAP funding with respect to median PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Which city had the 4th lowest NCAP funding with respect to its median PM2.5 concentration in 2022 (FY 2021-22)?,Dewas 585,727,funding_based,Which state has the 3rd lowest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 3rd lowest NCAP funding relative to its median PM2.5 concentration in 2021 (FY 2020-21).,Maharashtra 586,728,funding_based,Which state has the 3rd highest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Report the state with the 3rd highest NCAP funding considering its median PM10 concentration in 2020 (FY 2019-20).,Maharashtra 587,730,funding_based,Which city has the 5th lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Which city received the 5th lowest NCAP funding relative to its total PM2.5 concentration in 2020 (FY 2019-20)?,Gaya 588,731,funding_based,Which state has the 4th highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Identify the state with the 4th highest NCAP funding considering its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Madhya Pradesh 589,732,funding_based,Which city has the highest NCAP funding with respect to total PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the highest NCAP funding with respect to its total PM10 concentration in 2021 (FY 2020-21).,Akola 590,733,funding_based,Which state has the 2nd lowest NCAP funding with respect to average PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Determine which state had the 2nd lowest NCAP funding relative to its average PM2.5 concentration in 2021 (FY 2020-21).,Madhya Pradesh 591,734,funding_based,Which state has the 3rd lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Which state got the 3rd lowest NCAP funding considering its 25th percentile of PM2.5 concentration in 2020 (FY 2019-20)?,Odisha 592,735,funding_based,Which city has the 3rd lowest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 3rd lowest NCAP funding with respect to its median PM2.5 concentration in 2021 (FY 2020-21).,Dewas 593,736,funding_based,Which state has the 5th highest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Report the state with the 5th highest NCAP funding relative to its total PM2.5 concentration in 2022 (FY 2021-22).,Chandigarh 594,737,funding_based,Which city has the 4th lowest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 4th lowest NCAP funding considering its average PM10 concentration in 2021 (FY 2020-21).,Sagar 595,740,funding_based,Which state has the 2nd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state with the 2nd highest NCAP funding considering its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20).,Chandigarh 596,741,funding_based,Which state has the 2nd highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Determine which state got the 2nd highest NCAP funding with respect to its average PM10 concentration in 2021 (FY 2020-21).,Jammu and Kashmir 597,743,funding_based,Which city has the 3rd lowest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Identify the city with the 3rd lowest NCAP funding considering the variance of its PM10 concentration in 2021 (FY 2020-21).,Solapur 598,744,funding_based,Which city has the 4th highest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th highest NCAP funding with respect to its total PM2.5 concentration in 2021 (FY 2020-21).,Anantapur 599,746,funding_based,Which state has the lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Which state got the lowest NCAP funding considering its 75th percentile of PM2.5 concentration in 2020 (FY 2019-20)?,Assam 600,747,funding_based,Which city has the lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city that received the lowest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Dewas 601,748,funding_based,Which state has the 2nd highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state with the 2nd highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Jammu and Kashmir 602,750,funding_based,Which city has the highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Which state had the highest NCAP funding with respect to its 25th percentile of PM10 concentration in 2020 (FY 2019-20)?,Pune 603,751,funding_based,Which city has the 2nd lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd lowest NCAP funding relative to the standard deviation of its PM10 concentration in 2021 (FY 2020-21).,Solapur 604,752,funding_based,Which city has the 4th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th highest NCAP funding considering the standard deviation of its PM2.5 concentration in 2022 (FY 2021-22).,Dehradun 605,753,funding_based,Which state has the 5th highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Determine which state got the 5th highest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Maharashtra 606,754,funding_based,Which state has the 4th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th lowest NCAP funding relative to the standard deviation of its PM10 concentration in 2022 (FY 2021-22)?,Himachal Pradesh 607,755,funding_based,Which city has the lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city with the lowest NCAP funding considering its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Byrnihat 608,756,funding_based,Which state has the 3rd highest NCAP funding with respect to median PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 3rd highest NCAP funding with respect to its median PM2.5 concentration in 2021 (FY 2020-21).,Tamil Nadu 609,757,funding_based,Which state has the highest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state had the highest NCAP funding relative to its average PM2.5 concentration in 2022 (FY 2021-22).,Jammu and Kashmir 610,759,funding_based,Which state has the 2nd lowest NCAP funding with respect to average PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Identify the state that received the 2nd lowest NCAP funding with respect to its average PM2.5 concentration in 2022 (FY 2021-22).,Meghalaya 611,760,funding_based,Which city has the lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city with the lowest NCAP funding relative to its total PM2.5 concentration in 2021 (FY 2020-21).,Ujjain 612,761,funding_based,Which city has the 2nd lowest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 2nd lowest NCAP funding considering its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Patiala 613,762,funding_based,Which state has the 2nd lowest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['state'] print(lowest_funding_city) true_code()",Which state had the 2nd lowest NCAP funding with respect to its median PM10 concentration in 2020 (FY 2019-20)?,Odisha 614,765,funding_based,Which state has the highest NCAP funding with respect to average PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].mean().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state got the highest NCAP funding with respect to its average PM10 concentration in 2021 (FY 2020-21).,Meghalaya 615,766,funding_based,Which state has the 3rd lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['state'] print(lowest_funding_city) true_code()",Which state received the 3rd lowest NCAP funding relative to the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21)?,Maharashtra 616,767,funding_based,Which state has the 5th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state with the 5th lowest NCAP funding considering its 25th percentile of PM2.5 concentration in 2022 (FY 2021-22).,Himachal Pradesh 617,768,funding_based,Which city has the 4th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th lowest NCAP funding with respect to the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Khanna 618,769,funding_based,Which state has the highest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['state'] print(lowest_funding_city) true_code()",Determine which state had the highest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Meghalaya 619,770,funding_based,Which state has the 5th lowest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Which state got the 5th lowest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21)?,Maharashtra 620,771,funding_based,Which city has the 4th highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2020 (FY 2019-20).,Chandigarh 621,772,funding_based,Which city has the 5th lowest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Report the city with the 5th lowest NCAP funding relative to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Solapur 622,773,funding_based,Which state has the 4th highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Determine the state which was granted the 4th highest NCAP funding considering its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,Jammu and Kashmir 623,776,funding_based,Which city has the 4th lowest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th lowest NCAP funding considering its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21).,Moradabad 624,777,funding_based,Which city has the 5th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['city'] print(lowest_funding_city) true_code()",Determine which city got the 5th lowest NCAP funding with respect to the standard deviation of its PM10 concentration in 2021 (FY 2020-21).,Firozabad 625,778,funding_based,Which state has the 4th lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state received the 4th lowest NCAP funding relative to its total PM2.5 concentration in 2020 (FY 2019-20)?,Uttar Pradesh 626,779,funding_based,Which city has the lowest NCAP funding with respect to total PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city with the lowest NCAP funding considering its total PM2.5 concentration in 2022 (FY 2021-22).,Byrnihat 627,780,funding_based,Which city has the 4th highest NCAP funding with respect to total PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city that was granted the 4th highest NCAP funding with respect to its total PM10 concentration in 2022 (FY 2021-22).,Sangli 628,781,funding_based,Which city has the 3rd highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city had the 3rd highest NCAP funding relative to its 25th percentile of PM10 concentration in 2020 (FY 2019-20).,Hyderabad 629,783,funding_based,Which city has the 5th highest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 5th highest NCAP funding with respect to its total PM2.5 concentration in 2021 (FY 2020-21).,Badlapur 630,784,funding_based,Which city has the 4th lowest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Report the city with the 4th lowest NCAP funding relative to the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,Gaya 631,785,funding_based,Which city has the 2nd lowest NCAP funding with respect to variance of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[1]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 2nd lowest NCAP funding considering the variance of its PM2.5 concentration in 2020 (FY 2019-20).,Moradabad 632,786,funding_based,Which state has the 4th highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Which state had the 4th highest NCAP funding with respect to its 75th percentile of PM10 concentration in 2021 (FY 2020-21)?,Tamil Nadu 633,787,funding_based,Which city has the 4th highest NCAP funding with respect to 25th percentile of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th highest NCAP funding relative to its 25th percentile of PM10 concentration in 2021 (FY 2020-21).,Srinagar 634,789,funding_based,Which city has the 3rd lowest NCAP funding with respect to average PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].mean().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city got the 3rd lowest NCAP funding with respect to its average PM2.5 concentration in 2020 (FY 2019-20).,Patiala 635,792,funding_based,Which state has the 3rd highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[2]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 3rd highest NCAP funding with respect to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Jammu and Kashmir 636,794,funding_based,Which state has the 4th lowest NCAP funding with respect to total PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Which state got the 4th lowest NCAP funding considering its total PM2.5 concentration in 2021 (FY 2020-21)?,Uttar Pradesh 637,795,funding_based,Which city has the 4th highest NCAP funding with respect to standard deviation of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 4th highest NCAP funding with respect to the standard deviation of its PM10 concentration in 2022 (FY 2021-22).,Dehradun 638,796,funding_based,Which state has the 4th highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state with the 4th highest NCAP funding relative to its 75th percentile of PM10 concentration in 2022 (FY 2021-22).,Karnataka 639,797,funding_based,Which state has the 5th lowest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[4]['state'] print(lowest_funding_city) true_code()",Determine which state was granted the 5th lowest NCAP funding considering the standard deviation of its PM2.5 concentration in 2021 (FY 2020-21).,Punjab 640,799,funding_based,Which city has the 2nd highest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['city'] print(lowest_funding_city) true_code()",Identify the city that received the 2nd highest NCAP funding relative to its total PM2.5 concentration in 2020 (FY 2019-20).,Angul 641,800,funding_based,Which city has the lowest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['city'] print(lowest_funding_city) true_code()",Report the city with the lowest NCAP funding considering its total PM10 concentration in 2020 (FY 2019-20).,Khanna 642,801,funding_based,Which city has the 5th highest NCAP funding with respect to variance of PM 10 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM10'].var().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Determine which city got the 5th highest NCAP funding with respect to the variance of its PM10 concentration in 2021 (FY 2020-21).,Kohima 643,803,funding_based,Which city has the highest NCAP funding with respect to standard deviation of PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].std().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[0]['city'] print(lowest_funding_city) true_code()",Identify the city with the highest NCAP funding considering the standard deviation of its PM2.5 concentration in 2020 (FY 2019-20).,Nagpur 644,806,funding_based,Which state has the 2nd highest NCAP funding with respect to 75th percentile of PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM10'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Which state got the 2nd highest NCAP funding considering its 75th percentile of PM10 concentration in 2022 (FY 2021-22)?,Uttar Pradesh 645,809,funding_based,Which city has the 4th lowest NCAP funding with respect to total PM 2.5 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('city')['PM2.5'].sum().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['city'] print(lowest_funding_city) true_code()",Determine which city was granted the 4th lowest NCAP funding considering its total PM2.5 concentration in 2020 (FY 2019-20).,Alwar 646,810,funding_based,Which city has the 5th highest NCAP funding with respect to 25th percentile of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('city')['PM2.5'].quantile(0.25).reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['city'] print(lowest_funding_city) true_code()",Which city had the 5th highest NCAP funding with respect to its 25th percentile of PM2.5 concentration in 2021 (FY 2020-21)?,Kohima 647,812,funding_based,Which state has the 4th lowest NCAP funding with respect to total PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].sum().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[3]['state'] print(lowest_funding_city) true_code()",Report the state with the 4th lowest NCAP funding considering its total PM10 concentration in 2020 (FY 2019-20).,Uttar Pradesh 648,813,funding_based,Which city has the 3rd lowest NCAP funding with respect to median PM 10 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('city')['PM10'].median().reset_index() funding_year = ncap_funding_data[['city', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='city', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[2]['city'] print(lowest_funding_city) true_code()",Determine which city got the 3rd lowest NCAP funding with respect to its median PM10 concentration in 2022 (FY 2021-22).,Kohima 649,814,funding_based,Which state has the lowest NCAP funding with respect to 75th percentile of PM 2.5 concentration in 2022 (FY 2021-22)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] city_pm_year = data_year.groupby('state')['PM2.5'].quantile(0.75).reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2021-22']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2021-22'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm').iloc[0]['state'] print(lowest_funding_city) true_code()",Which state received the lowest NCAP funding relative to its 75th percentile of PM2.5 concentration in 2022 (FY 2021-22)?,Himachal Pradesh 650,815,funding_based,Which state has the 5th highest NCAP funding with respect to median PM 10 concentration in 2020 (FY 2019-20)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] city_pm_year = data_year.groupby('state')['PM10'].median().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2019-20']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2019-20'] / merged_df['PM10'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[4]['state'] print(lowest_funding_city) true_code()",Identify the state with the 5th highest NCAP funding considering its median PM10 concentration in 2020 (FY 2019-20).,Maharashtra 651,816,funding_based,Which state has the 2nd highest NCAP funding with respect to variance of PM 2.5 concentration in 2021 (FY 2020-21)?,"def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] city_pm_year = data_year.groupby('state')['PM2.5'].var().reset_index() funding_year = ncap_funding_data[['state', 'Amount released during FY 2020-21']] merged_df = city_pm_year.merge(funding_year, on='state', how='inner') merged_df['funding_per_pm'] = merged_df['Amount released during FY 2020-21'] / merged_df['PM2.5'] lowest_funding_city = merged_df.sort_values('funding_per_pm', ascending=False).iloc[1]['state'] print(lowest_funding_city) true_code()",Report the state that was granted the 2nd highest NCAP funding with respect to the variance of its PM2.5 concentration in 2021 (FY 2020-21).,Chhattisgarh 652,818,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Which state ranked as the 3rd least polluted based on per capita PM10 exposure during 2020?,Uttar Pradesh 653,819,population_based,Which state was the lowest polluted in terms of per capita PM 2.5 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Identify the state that was the least polluted in terms of per capita PM2.5 exposure in 2022.,Uttar Pradesh 654,821,population_based,Which state was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state was the 2nd most polluted regarding per capita PM2.5 exposure in 2018?,Haryana 655,823,population_based,Which state was the highest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the most polluted state based on per capita PM10 exposure during 2023.,Chandigarh 656,824,population_based,Which state was the lowest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Determine the state which was the least polluted in terms of per capita PM10 exposure in 2022.,Uttar Pradesh 657,825,population_based,Which state was the lowest polluted in terms of per capita PM 10 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Which state ranked as the least polluted regarding per capita PM10 exposure in 2021?,Tamil Nadu 658,827,population_based,Which state was the 3rd highest polluted in terms of per capita PM 2.5 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the state that was the 3rd most polluted concerning per capita PM2.5 exposure in 2021.,Puducherry 659,828,population_based,Which state was the 2nd highest polluted in terms of per capita PM 10 exposure in 2024?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2024] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Determine the 2nd most polluted state based on per capita PM10 exposure during 2024.,Sikkim 660,829,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Which state was the 3rd least polluted in terms of per capita PM10 exposure in 2023?,Maharashtra 661,830,population_based,Which state was the 5th lowest polluted in terms of per capita PM 10 exposure in 2024?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2024] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[4]['state'] print(required_state) true_code() ",Report the state ranking as the 5th least polluted regarding per capita PM10 exposure in 2024.,West Bengal 662,831,population_based,Which state was the lowest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Identify the least polluted state concerning per capita PM2.5 exposure in 2018.,Maharashtra 663,833,population_based,Which state was the 2nd highest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state was the 2nd most polluted in terms of per capita PM10 exposure in 2022?,Puducherry 664,834,population_based,Which state was the 3rd highest polluted in terms of per capita PM 10 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the state ranking 3rd highest in pollution from per capita PM10 exposure for 2021.,Arunachal Pradesh 665,836,population_based,Which state was the 5th lowest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[4]['state'] print(required_state) true_code() ",Determine the state which was the 5th least polluted regarding per capita PM10 exposure in 2018.,Andhra Pradesh 666,837,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Which state ranked as the 2nd least polluted based on per capita PM10 exposure during 2019?,Maharashtra 667,838,population_based,Which state was the lowest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Report the least polluted state in terms of per capita PM10 exposure in 2023.,Uttar Pradesh 668,840,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Determine the 3rd least polluted state based on per capita PM10 exposure during 2021.,Maharashtra 669,841,population_based,Which state was the 5th lowest polluted in terms of per capita PM 2.5 exposure in 2024?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2024] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[4]['state'] print(required_state) true_code() ",Which state was the 5th least polluted regarding per capita PM2.5 exposure in 2024?,West Bengal 670,843,population_based,Which state was the highest polluted in terms of per capita PM 10 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the most polluted state concerning per capita PM10 exposure in 2019.,Chandigarh 671,844,population_based,Which state was the 5th lowest polluted in terms of per capita PM 2.5 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[4]['state'] print(required_state) true_code() ",Determine the state which was the 5th least polluted based on per capita PM2.5 exposure during 2019.,Kerala 672,845,population_based,Which state was the 3rd highest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Which state ranked 3rd highest in pollution from per capita PM10 exposure for 2022?,Sikkim 673,846,population_based,Which state was the 3rd highest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the 3rd most polluted state regarding per capita PM10 exposure in 2018.,Jharkhand 674,849,population_based,Which state was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state was the 2nd most polluted based on per capita PM2.5 exposure during 2021?,Nagaland 675,851,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Identify the 2nd least polluted state in terms of per capita PM2.5 exposure in 2023.,Maharashtra 676,852,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Determine the state which was the 3rd least polluted concerning per capita PM2.5 exposure in 2018.,Karnataka 677,853,population_based,Which state was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Which state ranked as the 3rd least polluted based on per capita PM10 exposure during 2019?,Uttar Pradesh 678,854,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Report the 2nd least polluted state regarding per capita PM2.5 exposure in 2018.,Uttar Pradesh 679,855,population_based,Which state was the 3rd highest polluted in terms of per capita PM 10 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the 3rd most polluted state in terms of per capita PM10 exposure in 2020.,Tripura 680,857,population_based,Which state was the 5th highest polluted in terms of per capita PM 10 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Which state was the 5th most polluted based on per capita PM10 exposure during 2020?,Delhi 681,858,population_based,Which state was the 3rd highest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the state ranking 3rd highest in pollution from per capita PM2.5 exposure for 2018.,Punjab 682,860,population_based,Which state was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Determine the state which was the 2nd least polluted in terms of per capita PM10 exposure in 2018.,Uttar Pradesh 683,861,population_based,Which state was the highest polluted in terms of per capita PM 2.5 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() merged_df = state_pm_avg.merge(states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which state ranked as the most polluted concerning per capita PM2.5 exposure in 2020?,Chandigarh 684,864,population_based,Which union territory was the lowest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Identify the union territory that was least polluted regarding per capita PM2.5 exposure in 2018.,Delhi 685,865,population_based,Which union territory was the 3rd lowest polluted in terms of per capita PM 2.5 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Determine the union territory ranking as the 3rd least polluted concerning per capita PM2.5 exposure for 2020.,Jammu and Kashmir 686,866,population_based,Which union territory was the highest polluted in terms of per capita PM 10 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which union territory was the most polluted based on per capita PM10 exposure in 2019?,Chandigarh 687,867,population_based,Which union territory was the lowest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Report the union territory that was least polluted in terms of per capita PM10 exposure in 2023.,Jammu and Kashmir 688,868,population_based,Which union territory was the 3rd highest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the 3rd most polluted union territory regarding per capita PM2.5 exposure in 2018.,Jammu and Kashmir 689,869,population_based,Which union territory was the lowest polluted in terms of per capita PM 10 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Determine the least polluted union territory concerning per capita PM10 exposure in 2021.,Jammu and Kashmir 690,870,population_based,Which union territory was the highest polluted in terms of per capita PM 2.5 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which union territory was the most polluted based on per capita PM2.5 exposure during 2022?,Chandigarh 691,871,population_based,Which union territory was the lowest polluted in terms of per capita PM 2.5 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Report the union territory ranking as the least polluted in terms of per capita PM2.5 exposure in 2022.,Jammu and Kashmir 692,875,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Report the 2nd least polluted union territory in terms of per capita PM10 exposure in 2018.,Chandigarh 693,876,population_based,Which union territory was the 3rd highest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the 3rd most polluted union territory regarding per capita PM10 exposure for 2022.,Delhi 694,877,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 10 exposure in 2024?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2024] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Determine the 2nd most polluted union territory concerning per capita PM10 exposure in 2024.,Puducherry 695,878,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which union territory was the 2nd most polluted based on per capita PM10 exposure during 2023?,Puducherry 696,879,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Report the 2nd least polluted union territory in terms of per capita PM2.5 exposure in 2023.,Delhi 697,881,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Determine the 2nd most polluted union territory concerning per capita PM2.5 exposure in 2018.,Chandigarh 698,882,population_based,Which union territory was the 2nd highest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which union territory was the 2nd most polluted based on per capita PM10 exposure during 2018?,Chandigarh 699,883,population_based,Which union territory was the lowest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Report the least polluted union territory in terms of per capita PM10 exposure in 2022.,Jammu and Kashmir 700,884,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Identify the 2nd least polluted union territory regarding per capita PM2.5 exposure for 2020.,Chandigarh 701,885,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 10 exposure in 2022?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2022] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Determine the 2nd least polluted union territory concerning per capita PM10 exposure in 2022.,Delhi 702,886,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Which union territory was the 2nd least polluted based on per capita PM2.5 exposure during 2018?,Chandigarh 703,887,population_based,Which union territory was the lowest polluted in terms of per capita PM 10 exposure in 2020?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2020] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Report the least polluted union territory in terms of per capita PM10 exposure in 2020.,Delhi 704,888,population_based,Which union territory was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2023?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2023] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Identify the 3rd least polluted union territory regarding per capita PM10 exposure for 2023.,Puducherry 705,891,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2021?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2021] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Report the 2nd least polluted union territory in terms of per capita PM2.5 exposure in 2021.,Delhi 706,892,population_based,Which union territory was the 2nd lowest polluted in terms of per capita PM 2.5 exposure in 2024?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2024] state_pm_avg = data_year.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM2.5'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[1]['state'] print(required_state) true_code() ",Identify the 2nd least polluted union territory regarding per capita PM2.5 exposure for 2024.,Delhi 707,896,population_based,Which union territory was the 3rd lowest polluted in terms of per capita PM 10 exposure in 2019?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2019] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[2]['state'] print(required_state) true_code() ",Identify the 3rd least polluted union territory regarding per capita PM10 exposure for 2019.,Jammu and Kashmir 708,897,population_based,Which union territory was the lowest polluted in terms of per capita PM 10 exposure in 2018?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") data_year = main_data[main_data['Timestamp'].dt.year == 2018] state_pm_avg = data_year.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state',how='inner') merged_df['per_capita_pm'] = (merged_df['PM10'] / merged_df['population']) * 1000000 required_state = merged_df.sort_values('per_capita_pm').iloc[0]['state'] print(required_state) true_code() ",Determine the least polluted union territory concerning per capita PM10 exposure in 2018.,Delhi 709,898,population_based,"Among states with a population above the median population, which one receives the 2nd highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].median() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Report which state, among those with a population exceeding the median, receives the 2nd highest per capita NCAP funding.",Maharashtra 710,900,population_based,"Among states with a population below the median population, which one receives the 2nd highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].median() merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Determine the state, from those with a population less than the median, which receives the 2nd highest per capita NCAP funding.",Nagaland 711,902,population_based,"Among states with a population below the average population, which one receives the 2nd highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].mean() merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ","Identify the state, among those with a population below the average, that secures the 2nd highest per capita NCAP funding.",Nagaland 712,903,population_based,"Among states with a population above the 25th percentile population, which one receives the 3rd lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[2]['state'] print(required_state) true_code() ","Report which state, from those with populations above the 25th percentile, receives the 3rd lowest per capita NCAP funding.",Gujarat 713,904,population_based,"Among states with a population below the 25th percentile population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Among states with a population below the 25th percentile, determine the one that obtains the highest per capita NCAP funding.",Chandigarh 714,905,population_based,"Among states with a population above the median population, which one receives the 2nd lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].median() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[1]['state'] print(required_state) true_code() ","Which state, out of those with populations exceeding the median, is allocated the 2nd lowest per capita NCAP funding?",Jharkhand 715,906,population_based,"Among states with a population below the 25th percentile population, which one receives the lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state'] print(required_state) true_code() ","Identify the state, from those with a population less than the 25th percentile, which receives the lowest per capita NCAP funding.",Meghalaya 716,908,population_based,"Among states with a population below the average population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].mean() merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Determine the state, from those with populations below the average, that obtains the highest per capita NCAP funding.",Chandigarh 717,909,population_based,"Among states with a population above the median population, which one receives the lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].median() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state'] print(required_state) true_code() ","Which state, out of those with populations exceeding the median, is allocated the lowest per capita NCAP funding?",Tamil Nadu 718,911,population_based,"Among states with a population above the 75th percentile population, which one receives the 3rd lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.75) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[2]['state'] print(required_state) true_code() ","Report which state, from those with populations above the 75th percentile, secures the 3rd lowest per capita NCAP funding.",West Bengal 719,912,population_based,"Among states with a population above the average population, which one receives the 3rd lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].mean() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[2]['state'] print(required_state) true_code() ","Determine the state, out of those with populations above the average, that obtains the 3rd lowest per capita NCAP funding.",Bihar 720,913,population_based,"Among states with a population above the 25th percentile population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state, among those with populations exceeding the 25th percentile, is allocated the highest per capita NCAP funding?",Jammu and Kashmir 721,914,population_based,"Among states with a population above the average population, which one receives the 2nd lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].mean() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[1]['state'] print(required_state) true_code() ","Identify the state, from those with a population above the average, which receives the 2nd lowest per capita NCAP funding.",Gujarat 722,915,population_based,"Among states with a population above the 25th percentile population, which one receives the 3rd highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ","Report which state, out of those with populations exceeding the 25th percentile, secures the 3rd highest per capita NCAP funding.",Himachal Pradesh 723,916,population_based,"Among states with a population above the 75th percentile population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].quantile(0.75) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Determine the state, among those with populations above the 75th percentile, that obtains the highest per capita NCAP funding.",Maharashtra 724,917,population_based,"Among states with a population above the average population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() merged_df = pd.merge(states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = states_data['population'].mean() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Which state, from those with populations exceeding the average, is allocated the highest per capita NCAP funding?",Andhra Pradesh 725,918,population_based,"Among union territories with a population below the average population, which one receives the lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].mean() merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state'] print(required_state) true_code() ","Among union territories with a population below the average, identify the one that receives the lowest per capita NCAP funding.",Chandigarh 726,919,population_based,"Among union territories with a population above the average population, which one receives the lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].mean() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state'] print(required_state) true_code() ","Report which union territory, out of those with populations above the average, obtains the lowest per capita NCAP funding.",Delhi 727,920,population_based,"Among union territories with a population above the 25th percentile population, which one receives the lowest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita').iloc[0]['state'] print(required_state) true_code() ","Determine the union territory, from those with populations exceeding the 25th percentile, which is allocated the lowest per capita NCAP funding.",Delhi 728,922,population_based,"Among union territories with a population above the 25th percentile population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].quantile(0.25) merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Identify the union territory, out of those with populations above the 25th percentile, that secures the highest per capita NCAP funding.",Jammu and Kashmir 729,923,population_based,"Among union territories with a population above the median population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].median() merged_df = merged_df[merged_df['population'] > required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Report which union territory, from those with populations exceeding the median, obtains the highest per capita NCAP funding.",Jammu and Kashmir 730,924,population_based,"Among union territories with a population below the average population, which one receives the highest per capita NCAP funding?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = pd.merge(filtered_states_data, state_funding, on='state') merged_df['funding_per_capita'] = merged_df['Total fund released'] / merged_df['population'] required_pop = filtered_states_data['population'].mean() merged_df = merged_df[merged_df['population'] < required_pop] required_state = merged_df.sort_values('funding_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ","Determine the union territory, among those with a population below the average, which is allocated the highest per capita NCAP funding.",Chandigarh 731,926,population_based,Which state in India has the highest number of monitoring stations relative to its population?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() merged_df = station_counts.merge(states_data, on='state', how='inner') merged_df['stations_per_million'] = merged_df['station'] / merged_df['population'] required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Report the state in India with the highest number of monitoring stations when considering its population.,Chandigarh 732,928,population_based,Which state in India has the lowest number of monitoring stations relative to its population?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() merged_df = station_counts.merge(states_data, on='state', how='inner') merged_df['stations_per_million'] = merged_df['station'] / merged_df['population'] required_state = merged_df.sort_values('stations_per_million').iloc[0]['state'] print(required_state) true_code() ",Which state in India possesses the smallest count of monitoring stations in relation to its population?,Jammu and Kashmir 733,929,population_based,Which state in India has the 4th highest number of monitoring stations relative to its population?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() merged_df = station_counts.merge(states_data, on='state', how='inner') merged_df['stations_per_million'] = merged_df['station'] / merged_df['population'] required_state = merged_df.sort_values('stations_per_million', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Identify the state in India that has the 4th most monitoring stations adjusted for its population size.,Haryana 734,932,population_based,Which union territory in India has the lowest number of monitoring stations relative to its population?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") station_counts = main_data.groupby('state')['station'].nunique().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = station_counts.merge(filtered_states_data, on='state', how='inner') merged_df['stations_per_million'] = merged_df['station'] / merged_df['population'] required_state = merged_df.sort_values('stations_per_million').iloc[0]['state'] print(required_state) true_code() ",Which union territory in India has the lowest count of monitoring stations when considering its population?,Jammu and Kashmir 735,934,population_based,Which state has the 4th highest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Which state demonstrates the 4th highest standard deviation of PM10 concentration relative to its population density?,Meghalaya 736,936,population_based,Which state has the 2nd lowest standard deviation of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Report the state showing the 2nd lowest standard deviation of PM2.5 concentration in relation to its population density.,Puducherry 737,937,population_based,Which state has the 5th lowest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[4]['state'] print(required_state) true_code() ",Determine the state that has the 5th lowest average PM10 concentration adjusted for population density.,West Bengal 738,938,population_based,Which state has the 2nd highest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state exhibits the 2nd highest average PM2.5 concentration relative to its population density?,Himachal Pradesh 739,939,population_based,Which state has the lowest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Identify the state possessing the lowest 25th percentile of PM10 concentration when considering population density.,Chandigarh 740,940,population_based,Which state has the 3rd highest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the state with the 3rd highest variance of PM10 concentration normalized by population density.,Rajasthan 741,942,population_based,Which state has the 4th highest 75th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Which state demonstrates the 4th highest 75th percentile of PM2.5 concentration adjusted for population density?,Rajasthan 742,944,population_based,Which state has the 5th lowest median PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[4]['state'] print(required_state) true_code() ",Report the state exhibiting the 5th lowest median PM2.5 concentration when considering population density.,West Bengal 743,945,population_based,Which state has the 3rd lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Determine the state that has the 3rd lowest average PM2.5 concentration normalized by population density.,Delhi 744,946,population_based,Which state has the 3rd lowest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Which state shows the 3rd lowest standard deviation of PM10 concentration in relation to its population density?,Delhi 745,947,population_based,Which state has the highest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the state possessing the highest 25th percentile of PM2.5 concentration adjusted for population density.,Arunachal Pradesh 746,948,population_based,Which state has the 5th highest 75th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Report the state with the 5th highest 75th percentile of PM2.5 concentration relative to its population density.,Manipur 747,949,population_based,Which state has the 3rd highest median PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Determine which state exhibits the 3rd highest median PM10 concentration when considering population density.,Rajasthan 748,950,population_based,Which state has the lowest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Which state demonstrates the lowest variance of PM10 concentration normalized by population density?,Puducherry 749,951,population_based,Which state has the highest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the state showing the highest 75th percentile of PM10 concentration in relation to its population density.,Arunachal Pradesh 750,952,population_based,Which state has the 5th highest median PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Report the state with the 5th highest median PM2.5 concentration adjusted for population density.,Uttarakhand 751,953,population_based,Which state has the 4th highest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Determine the state that has the 4th highest 25th percentile of PM10 concentration relative to its population density.,Nagaland 752,955,population_based,Which state has the 5th lowest 75th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[4]['state'] print(required_state) true_code() ",Identify the state possessing the 5th lowest 75th percentile of PM2.5 concentration normalized by population density.,Tamil Nadu 753,957,population_based,Which state has the lowest variance of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Determine which state shows the lowest variance of PM2.5 concentration adjusted for population density.,Puducherry 754,959,population_based,Which state has the highest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the state with the highest 25th percentile of PM10 concentration when considering population density.,Arunachal Pradesh 755,960,population_based,Which state has the 5th highest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Report the state exhibiting the 5th highest 25th percentile of PM10 concentration normalized by population density.,Mizoram 756,961,population_based,Which state has the 4th lowest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Determine the state that has the 4th lowest 25th percentile of PM10 concentration in relation to its population density.,Kerala 757,963,population_based,Which state has the highest variance of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].var().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the state possessing the highest variance of PM2.5 concentration relative to its population density.,Manipur 758,964,population_based,Which state has the lowest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Report the state with the lowest standard deviation of PM10 concentration when considering population density.,Chandigarh 759,965,population_based,Which state has the 4th highest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Determine which state exhibits the 4th highest 75th percentile of PM10 concentration normalized by population density.,Rajasthan 760,966,population_based,Which state has the 4th highest standard deviation of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Which state demonstrates the 4th highest standard deviation of PM2.5 concentration in relation to its population density?,Mizoram 761,967,population_based,Which state has the 4th lowest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Identify the state showing the 4th lowest standard deviation of PM10 concentration adjusted for population density.,Kerala 762,968,population_based,Which state has the 2nd highest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Report the state with the 2nd highest total PM10 concentration relative to its population density.,Maharashtra 763,970,population_based,Which state has the 3rd highest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Which state demonstrates the 3rd highest 25th percentile of PM10 concentration normalized by population density?,Rajasthan 764,971,population_based,Which state has the 5th highest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Identify the state with the 5th highest standard deviation of PM10 concentration in relation to its population density.,Manipur 765,972,population_based,Which state has the 3rd lowest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Report the state showing the 3rd lowest total PM10 concentration adjusted for population density.,Sikkim 766,973,population_based,Which state has the lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Determine the state that has the lowest average PM2.5 concentration relative to its population density.,Chandigarh 767,974,population_based,Which state has the 2nd highest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state exhibits the 2nd highest 75th percentile of PM10 concentration when considering population density?,Himachal Pradesh 768,976,population_based,Which state has the 2nd lowest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Report the state with the 2nd lowest 25th percentile of PM10 concentration in relation to its population density.,Delhi 769,977,population_based,Which state has the highest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Determine which state shows the highest standard deviation of PM10 concentration adjusted for population density.,Arunachal Pradesh 770,978,population_based,Which state has the 4th lowest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Which state demonstrates the 4th lowest total PM10 concentration relative to its population density?,Jammu and Kashmir 771,979,population_based,Which state has the 5th highest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[4]['state'] print(required_state) true_code() ",Identify the state with the 5th highest average PM2.5 concentration when considering population density.,Rajasthan 772,980,population_based,Which state has the 5th lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[4]['state'] print(required_state) true_code() ",Report the state exhibiting the 5th lowest average PM2.5 concentration normalized by population density.,West Bengal 773,981,population_based,Which state has the 3rd lowest median PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Determine the state that has the 3rd lowest median PM10 concentration in relation to its population density.,Delhi 774,982,population_based,Which state has the 2nd highest total PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state shows the 2nd highest total PM2.5 concentration adjusted for population density?,Maharashtra 775,983,population_based,Which state has the highest median PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Identify the state possessing the highest median PM2.5 concentration relative to its population density.,Arunachal Pradesh 776,985,population_based,Which state has the 2nd lowest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Determine which state exhibits the 2nd lowest variance of PM10 concentration normalized by population density.,Chandigarh 777,986,population_based,Which state has the highest median PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].median().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which state demonstrates the highest median PM10 concentration in relation to its population density?,Arunachal Pradesh 778,987,population_based,Which state has the 4th highest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Identify the state showing the 4th highest average PM10 concentration adjusted for population density.,Rajasthan 779,988,population_based,Which state has the 3rd highest 75th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the state with the 3rd highest 75th percentile of PM2.5 concentration relative to its population density.,Nagaland 780,989,population_based,Which state has the 4th highest total PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Determine which state exhibits the 4th highest total PM2.5 concentration when considering population density.,Uttar Pradesh 781,990,population_based,Which state has the 2nd highest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() merged_df = avg_pm.merge(states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which state demonstrates the 2nd highest 25th percentile of PM2.5 concentration normalized by population density?,Himachal Pradesh 782,991,population_based,Which union territory has the 2nd lowest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Which union territory has the 2nd lowest 25th percentile of PM10 concentration relative to its population density?,Delhi 783,994,population_based,Which union territory has the 2nd highest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Determine the union territory that has the 2nd highest 25th percentile of PM2.5 concentration adjusted for population density.,Puducherry 784,995,population_based,Which union territory has the 2nd highest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which union territory exhibits the 2nd highest average PM2.5 concentration relative to its population density?,Delhi 785,996,population_based,Which union territory has the 3rd lowest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Identify the union territory possessing the 3rd lowest 75th percentile of PM10 concentration when considering population density.,Delhi 786,997,population_based,Which union territory has the highest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Report the union territory with the highest 25th percentile of PM10 concentration normalized by population density.,Jammu and Kashmir 787,999,population_based,Which union territory has the lowest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Which union territory demonstrates the lowest standard deviation of PM10 concentration adjusted for population density?,Chandigarh 788,1000,population_based,Which union territory has the 2nd highest total PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Identify the union territory with the 2nd highest total PM2.5 concentration relative to its population density.,Jammu and Kashmir 789,1001,population_based,Which union territory has the 3rd highest standard deviation of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the union territory exhibiting the 3rd highest standard deviation of PM2.5 concentration when considering population density.,Puducherry 790,1002,population_based,Which union territory has the 2nd highest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Determine the union territory that has the 2nd highest 75th percentile of PM10 concentration normalized by population density.,Delhi 791,1003,population_based,Which union territory has the 3rd lowest 25th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Which union territory shows the 3rd lowest 25th percentile of PM10 concentration in relation to its population density?,Puducherry 792,1004,population_based,Which union territory has the 4th lowest 75th percentile of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Identify the union territory possessing the 4th lowest 75th percentile of PM10 concentration adjusted for population density.,Jammu and Kashmir 793,1005,population_based,Which union territory has the lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Report the union territory with the lowest average PM2.5 concentration relative to its population density.,Chandigarh 794,1006,population_based,Which union territory has the highest variance of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Determine which union territory exhibits the highest variance of PM2.5 concentration when considering population density.,Jammu and Kashmir 795,1007,population_based,Which union territory has the 2nd highest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which union territory demonstrates the 2nd highest variance of PM10 concentration normalized by population density?,Delhi 796,1008,population_based,Which union territory has the 3rd highest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the union territory with the 3rd highest standard deviation of PM10 concentration in relation to its population density.,Puducherry 797,1009,population_based,Which union territory has the 4th highest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Report the union territory showing the 4th highest 25th percentile of PM2.5 concentration adjusted for population density.,Chandigarh 798,1010,population_based,Which union territory has the 4th lowest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Determine the union territory that has the 4th lowest average PM10 concentration relative to its population density.,Jammu and Kashmir 799,1011,population_based,Which union territory has the 2nd lowest median PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Which union territory exhibits the 2nd lowest median PM10 concentration when considering population density?,Puducherry 800,1013,population_based,Which union territory has the 2nd lowest standard deviation of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Report the union territory with the 2nd lowest standard deviation of PM2.5 concentration in relation to its population density.,Puducherry 801,1014,population_based,Which union territory has the 3rd lowest standard deviation of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Determine which union territory shows the 3rd lowest standard deviation of PM10 concentration adjusted for population density.,Delhi 802,1016,population_based,Which union territory has the 3rd lowest 75th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Identify the union territory with the 3rd lowest 75th percentile of PM2.5 concentration when considering population density.,Delhi 803,1017,population_based,Which union territory has the highest median PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Report the union territory exhibiting the highest median PM10 concentration normalized by population density.,Jammu and Kashmir 804,1018,population_based,Which union territory has the 4th lowest median PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[3]['state'] print(required_state) true_code() ",Determine the union territory that has the 4th lowest median PM2.5 concentration in relation to its population density.,Jammu and Kashmir 805,1019,population_based,Which union territory has the 3rd highest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Which union territory shows the 3rd highest variance of PM10 concentration adjusted for population density?,Chandigarh 806,1020,population_based,Which union territory has the 3rd highest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the union territory possessing the 3rd highest average PM10 concentration relative to its population density.,Puducherry 807,1021,population_based,Which union territory has the highest median PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Report the union territory with the highest median PM2.5 concentration when considering population density.,Jammu and Kashmir 808,1022,population_based,Which union territory has the 2nd lowest total PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Determine which union territory exhibits the 2nd lowest total PM2.5 concentration normalized by population density.,Chandigarh 809,1023,population_based,Which union territory has the 2nd highest standard deviation of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[1]['state'] print(required_state) true_code() ",Which union territory demonstrates the 2nd highest standard deviation of PM2.5 concentration in relation to its population density?,Delhi 810,1024,population_based,Which union territory has the lowest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Identify the union territory showing the lowest total PM10 concentration adjusted for population density.,Puducherry 811,1025,population_based,Which union territory has the 2nd lowest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Report the union territory with the 2nd lowest 25th percentile of PM2.5 concentration relative to its population density.,Delhi 812,1026,population_based,Which union territory has the 3rd lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[2]['state'] print(required_state) true_code() ",Determine which union territory exhibits the 3rd lowest average PM2.5 concentration when considering population density.,Delhi 813,1028,population_based,Which union territory has the 2nd lowest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Identify the union territory with the 2nd lowest average PM2.5 concentration in relation to its population density.,Puducherry 814,1029,population_based,Which union territory has the 3rd highest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Report the union territory showing the 3rd highest total PM10 concentration adjusted for population density.,Chandigarh 815,1030,population_based,Which union territory has the lowest 25th percentile of PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[0]['state'] print(required_state) true_code() ",Determine the union territory that has the lowest 25th percentile of PM2.5 concentration relative to its population density.,Chandigarh 816,1031,population_based,Which union territory has the 4th highest total PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Which union territory exhibits the 4th highest total PM10 concentration when considering population density?,Puducherry 817,1032,population_based,Which union territory has the 3rd highest average PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[2]['state'] print(required_state) true_code() ",Identify the union territory possessing the 3rd highest average PM2.5 concentration normalized by population density.,Puducherry 818,1033,population_based,Which union territory has the 2nd lowest variance of PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Report the union territory with the 2nd lowest variance of PM10 concentration in relation to its population density.,Chandigarh 819,1034,population_based,Which union territory has the 2nd lowest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita').iloc[1]['state'] print(required_state) true_code() ",Determine which union territory shows the 2nd lowest average PM10 concentration adjusted for population density.,Puducherry 820,1035,population_based,Which union territory has the highest total PM 2.5 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM2.5'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[0]['state'] print(required_state) true_code() ",Which union territory demonstrates the highest total PM2.5 concentration relative to its population density?,Delhi 821,1036,population_based,Which union territory has the 4th highest average PM 10 concentration relative to its population density?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") avg_pm = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True] merged_df = avg_pm.merge(filtered_states_data, on='state', how='inner') merged_df['Population Density'] = merged_df['population'] / merged_df['area (km2)'] merged_df['pm_per_capita'] = merged_df['PM10'] / merged_df['Population Density'] required_state = merged_df.sort_values('pm_per_capita', ascending=False).iloc[3]['state'] print(required_state) true_code() ",Identify the union territory with the 4th highest average PM10 concentration when considering population density.,Chandigarh 822,1037,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) having the 2nd largest population among the top 10 most polluted states, based on 25th percentile of PM10 levels.",Bihar 823,1038,population_based,"Which state(excuding UTs) has the lowest population among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) with the smallest population among the top 3 most polluted states, when pollution is measured by standard deviation of PM2.5 levels.",Manipur 824,1039,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 5 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 2nd smallest population within the top 5 most polluted states, based on 75th percentile of PM10 levels.",Haryana 825,1040,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 3rd smallest population among the top 3 most polluted states, determined by total PM2.5 levels?",Uttar Pradesh 826,1041,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd smallest population among the top 10 most polluted states, based on median PM10 levels.",Haryana 827,1043,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd smallest population among the top 10 most polluted states, based on median PM10 levels.",Punjab 828,1044,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 3rd largest population within the top 5 most polluted states, determined by the 25th percentile of PM10 levels?",Jharkhand 829,1045,population_based,"Which state(excuding UTs) has the lowest population among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the smallest population among the top 3 most polluted states, based on variance of PM10 levels.",Assam 830,1046,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the smallest population among the top 10 most polluted states, when pollution is measured by median PM2.5 levels.",Tripura 831,1047,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the largest population within the top 5 most polluted states, based on total PM2.5 levels.",Uttar Pradesh 832,1049,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd largest population among the top 5 most polluted states, based on median PM2.5 levels.",Jharkhand 833,1051,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the largest population among the top 5 most polluted states, based on 75th percentile of PM2.5 levels.",Uttar Pradesh 834,1052,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd largest population within the top 5 most polluted states, determined by variance of PM2.5 levels?",Bihar 835,1053,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd smallest population among the top 10 most polluted states, based on average PM2.5 levels.",Haryana 836,1054,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the smallest population among the top 5 most polluted states, when pollution is measured by average PM10 levels.",Himachal Pradesh 837,1056,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the smallest population among the top 5 most polluted states, determined by the 25th percentile of PM2.5 levels?",Himachal Pradesh 838,1057,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 3 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd smallest population among the top 3 most polluted states, based on median PM2.5 levels.",Bihar 839,1058,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the smallest population within the top 5 most polluted states, when pollution is measured by total PM10 levels.",Haryana 840,1059,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 2nd smallest population among the top 10 most polluted states, based on total PM10 levels.",Punjab 841,1060,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd smallest population within the top 10 most polluted states, determined by the 75th percentile of PM2.5 levels?",Himachal Pradesh 842,1061,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the smallest population among the top 10 most polluted states, based on total PM10 levels.",Haryana 843,1063,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd smallest population within the top 5 most polluted states, based on total PM2.5 levels.",Bihar 844,1065,population_based,"Which state(excuding UTs) has the lowest population among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the smallest population among the top 3 most polluted states, based on 25th percentile of PM2.5 levels.",Himachal Pradesh 845,1068,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 3 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd smallest population within the top 3 most polluted states, determined by the 75th percentile of PM2.5 levels?",Bihar 846,1069,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd smallest population among the top 10 most polluted states, based on 75th percentile of PM10 levels.",Haryana 847,1070,population_based,"Which state(excuding UTs) has the highest population among the top 10 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the largest population among the top 10 most polluted states, when pollution is measured by 75th percentile of PM2.5 levels.",Uttar Pradesh 848,1071,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd largest population within the top 3 most polluted states, based on median PM10 levels.",Himachal Pradesh 849,1072,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the smallest population among the top 5 most polluted states, determined by the 25th percentile of PM10 levels?",Himachal Pradesh 850,1073,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd smallest population among the top 5 most polluted states, based on 25th percentile of PM10 levels.",Haryana 851,1074,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the smallest population within the top 5 most polluted states, when pollution is measured by total PM2.5 levels.",Haryana 852,1075,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 10 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd smallest population among the top 10 most polluted states, based on variance of PM10 levels.",Assam 853,1076,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 5 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 3rd smallest population within the top 5 most polluted states, determined by average PM2.5 levels?",Assam 854,1077,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 5 most polluted states, based on 25th percentile of PM10 levels.",Bihar 855,1080,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd largest population within the top 5 most polluted states, determined by median PM10 levels?",Bihar 856,1081,population_based,"Which state(excuding UTs) has the highest population among the top 3 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 3 most polluted states, based on median PM10 levels.",Bihar 857,1082,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd smallest population among the top 3 most polluted states, when pollution is measured by 25th percentile of PM2.5 levels.",Jharkhand 858,1083,population_based,"Which state(excuding UTs) has the lowest population among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the smallest population within the top 5 most polluted states, based on median PM2.5 levels.",Himachal Pradesh 859,1084,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the largest population among the top 5 most polluted states, determined by the 25th percentile of PM2.5 levels?",Bihar 860,1086,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd smallest population within the top 3 most polluted states, when pollution is measured by standard deviation of PM10 levels.",Uttar Pradesh 861,1087,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the smallest population among the top 10 most polluted states, based on 25th percentile of PM2.5 levels.",Tripura 862,1088,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd largest population within the top 3 most polluted states, determined by the 25th percentile of PM2.5 levels?",Haryana 863,1089,population_based,"Which state(excuding UTs) has the highest population among the top 3 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 3 most polluted states, based on 75th percentile of PM10 levels.",Uttar Pradesh 864,1090,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 10 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd smallest population among the top 10 most polluted states, when pollution is measured by variance of PM2.5 levels.",Himachal Pradesh 865,1091,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd largest population within the top 3 most polluted states, based on standard deviation of PM2.5 levels.",Manipur 866,1092,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd largest population among the top 5 most polluted states, determined by standard deviation of PM10 levels?",Bihar 867,1093,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd largest population among the top 5 most polluted states, based on total PM2.5 levels.",Maharashtra 868,1094,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd largest population within the top 3 most polluted states, when pollution is measured by 25th percentile of PM2.5 levels.",Himachal Pradesh 869,1095,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 5 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd smallest population among the top 5 most polluted states, based on 25th percentile of PM10 levels.",Jharkhand 870,1096,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 10 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 3rd largest population within the top 10 most polluted states, determined by the 25th percentile of PM10 levels?",Madhya Pradesh 871,1097,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd smallest population among the top 3 most polluted states, based on median PM2.5 levels.",Uttar Pradesh 872,1098,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 2nd largest population within the top 3 most polluted states, when pollution is measured by average PM2.5 levels.",Bihar 873,1099,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 3 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 2nd smallest population among the top 3 most polluted states, based on 75th percentile of PM10 levels.",Bihar 874,1101,population_based,"Which state(excuding UTs) has the highest population among the top 10 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 10 most polluted states, based on variance of PM10 levels.",Uttar Pradesh 875,1102,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 10 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd largest population among the top 10 most polluted states, when pollution is measured by variance of PM10 levels.",West Bengal 876,1103,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd largest population within the top 10 most polluted states, based on average PM2.5 levels.",West Bengal 877,1104,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 10 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 3rd smallest population among the top 10 most polluted states, determined by median PM2.5 levels?",Haryana 878,1105,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd smallest population among the top 10 most polluted states, based on average PM2.5 levels.",Himachal Pradesh 879,1106,population_based,"Which state(excuding UTs) has the highest population among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the largest population within the top 10 most polluted states, when pollution is measured by median PM10 levels.",Uttar Pradesh 880,1107,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the largest population among the top 5 most polluted states, based on 75th percentile of PM10 levels.",Uttar Pradesh 881,1108,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd smallest population within the top 10 most polluted states, determined by standard deviation of PM10 levels?",Haryana 882,1109,population_based,"Which state(excuding UTs) has the highest population among the top 10 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 10 most polluted states, based on total PM10 levels.",Uttar Pradesh 883,1110,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 2nd smallest population among the top 10 most polluted states, when pollution is measured by average PM10 levels.",Haryana 884,1111,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 2nd smallest population within the top 10 most polluted states, based on standard deviation of PM2.5 levels.",Tripura 885,1112,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 10 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd smallest population among the top 10 most polluted states, determined by variance of PM2.5 levels?",Tripura 886,1113,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd largest population among the top 10 most polluted states, based on average PM10 levels.",Madhya Pradesh 887,1114,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 2nd smallest population within the top 3 most polluted states, when pollution is measured by average PM2.5 levels.",Bihar 888,1115,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the smallest population among the top 10 most polluted states, based on average PM10 levels.",Himachal Pradesh 889,1117,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 10 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd largest population among the top 10 most polluted states, based on 75th percentile of PM10 levels.",Bihar 890,1118,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 5 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd largest population among the top 5 most polluted states, when pollution is measured by variance of PM10 levels.",Jharkhand 891,1119,population_based,"Which state(excuding UTs) has the lowest population among the top 3 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the smallest population within the top 3 most polluted states, based on total PM10 levels.",Haryana 892,1120,population_based,"Which state(excuding UTs) has the highest population among the top 5 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the largest population among the top 5 most polluted states, determined by average PM2.5 levels?",Uttar Pradesh 893,1121,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 3 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd smallest population among the top 3 most polluted states, based on variance of PM2.5 levels.",Uttar Pradesh 894,1122,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 5 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd largest population within the top 5 most polluted states, when pollution is measured by median PM10 levels.",Rajasthan 895,1123,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 2nd smallest population among the top 5 most polluted states, based on 25th percentile of PM2.5 levels.",Haryana 896,1125,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 3 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 2nd largest population among the top 3 most polluted states, based on 25th percentile of PM10 levels.",Haryana 897,1126,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 3 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 2nd largest population among the top 3 most polluted states, when pollution is measured by average PM10 levels.",Bihar 898,1127,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the 3rd largest population within the top 3 most polluted states, based on total PM10 levels.",Haryana 899,1128,population_based,"Which state(excuding UTs) has the 2nd highest population among the top 5 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd largest population among the top 5 most polluted states, determined by the 25th percentile of PM2.5 levels?",Jharkhand 900,1129,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the smallest population among the top 10 most polluted states, based on median PM10 levels.",Himachal Pradesh 901,1130,population_based,"Which state(excuding UTs) has the lowest population among the top 3 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the smallest population within the top 3 most polluted states, when pollution is measured by 75th percentile of PM2.5 levels.",Haryana 902,1131,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the smallest population among the top 10 most polluted states, based on standard deviation of PM10 levels.",Tripura 903,1132,population_based,"Which state(excuding UTs) has the lowest population among the top 10 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(10)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the smallest population within the top 10 most polluted states, determined by average PM2.5 levels?",Tripura 904,1133,population_based,"Which state(excuding UTs) has the highest population among the top 3 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the largest population among the top 3 most polluted states, based on average PM2.5 levels.",Uttar Pradesh 905,1134,population_based,"Which state(excuding UTs) has the 3rd highest population among the top 3 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[2]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 3rd largest population among the top 3 most polluted states, when pollution is measured by 25th percentile of PM10 levels.",Himachal Pradesh 906,1136,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which state (excluding UTs) possesses the 2nd smallest population among the top 3 most polluted states, determined by variance of PM10 levels?",Bihar 907,1137,population_based,"Which state(excuding UTs) has the 3rd lowest population among the top 5 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[2]['state'] print(max_population_state) true_code() ","Identify the state (excluding UTs) with the 3rd smallest population among the top 5 most polluted states, based on median PM2.5 levels.",Jharkhand 908,1138,population_based,"Which state(excuding UTs) has the 2nd lowest population among the top 5 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(5)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the state (excluding UTs) having the 2nd smallest population within the top 5 most polluted states, when pollution is measured by standard deviation of PM2.5 levels.",Haryana 909,1139,population_based,"Which state(excuding UTs) has the highest population among the top 3 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == False][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(3)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which state (excluding UTs) has the largest population among the top 3 most polluted states, based on variance of PM10 levels.",Uttar Pradesh 910,1140,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the union territory with the largest population among the top 2 most polluted union territories, based on 75th percentile of PM2.5 levels.",Delhi 911,1142,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the smallest population within the top 2 most polluted union territories, based on total PM2.5 levels.",Chandigarh 912,1144,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the union territory with the largest population among the top 4 most polluted union territories, based on average PM2.5 levels.",Delhi 913,1145,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second smallest population within the top 2 most polluted union territories, when pollution is measured by 75th percentile of PM10 levels.",Delhi 914,1146,population_based,"Which union territory has the lowest population among the top 4 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the smallest population among the top 4 most polluted union territories, based on 75th percentile of PM2.5 levels.",Chandigarh 915,1147,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which union territory possesses the smallest population within the top 2 most polluted union territories, determined by the 25th percentile of PM10 levels?",Chandigarh 916,1149,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Report the union territory having the largest population among the top 4 most polluted union territories, when pollution is measured by median PM2.5 levels.",Delhi 917,1150,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on median PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which union territory has the second smallest population within the top 4 most polluted union territories, based on median PM10 levels.",Puducherry 918,1152,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the union territory with the second smallest population among the top 2 most polluted union territories, based on 75th percentile of PM2.5 levels.",Delhi 919,1153,population_based,"Which union territory has the 2nd highest population among the top 2 most polluted states, based on 75th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second largest population within the top 2 most polluted union territories, when pollution is measured by 75th percentile of PM10 levels.",Chandigarh 920,1154,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the smallest population among the top 2 most polluted union territories, based on average PM2.5 levels.",Chandigarh 921,1155,population_based,"Which union territory has the lowest population among the top 4 most polluted states, based on total PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which union territory possesses the smallest population within the top 4 most polluted union territories, determined by total PM2.5 levels?",Chandigarh 922,1156,population_based,"Which union territory has the highest population among the top 4 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the union territory with the largest population among the top 4 most polluted union territories, based on 25th percentile of PM10 levels.",Delhi 923,1157,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second smallest population among the top 4 most polluted union territories, when pollution is measured by 25th percentile of PM2.5 levels.",Puducherry 924,1158,population_based,"Which union territory has the 2nd highest population among the top 4 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Determine which union territory has the second largest population within the top 4 most polluted union territories, based on total PM10 levels.",Jammu and Kashmir 925,1159,population_based,"Which union territory has the 2nd highest population among the top 2 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which union territory possesses the second largest population among the top 2 most polluted union territories, determined by standard deviation of PM10 levels?",Chandigarh 926,1160,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Identify the union territory with the largest population among the top 2 most polluted union territories, based on variance of PM2.5 levels.",Delhi 927,1161,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on 75th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the union territory having the smallest population within the top 2 most polluted union territories, when pollution is measured by 75th percentile of PM2.5 levels.",Chandigarh 928,1163,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on 25th percentile of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which union territory possesses the second smallest population within the top 2 most polluted union territories, determined by the 25th percentile of PM2.5 levels?",Delhi 929,1164,population_based,"Which union territory has the 2nd highest population among the top 2 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the union territory with the second largest population among the top 2 most polluted union territories, based on standard deviation of PM2.5 levels.",Chandigarh 930,1165,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on standard deviation of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second smallest population among the top 2 most polluted union territories, when pollution is measured by standard deviation of PM2.5 levels.",Delhi 931,1166,population_based,"Which union territory has the lowest population among the top 4 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the smallest population within the top 4 most polluted union territories, based on variance of PM2.5 levels.",Chandigarh 932,1167,population_based,"Which union territory has the 2nd highest population among the top 2 most polluted states, based on average PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Which union territory possesses the second largest population among the top 2 most polluted union territories, determined by average PM10 levels?",Chandigarh 933,1168,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the union territory with the second smallest population among the top 4 most polluted union territories, based on 25th percentile of PM10 levels.",Puducherry 934,1169,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second smallest population among the top 4 most polluted union territories, when pollution is measured by standard deviation of PM10 levels.",Puducherry 935,1171,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Which union territory possesses the largest population among the top 2 most polluted union territories, determined by median PM2.5 levels?",Delhi 936,1173,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Report the union territory having the smallest population within the top 2 most polluted union territories, when pollution is measured by variance of PM10 levels.",Chandigarh 937,1174,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the largest population among the top 2 most polluted union territories, based on total PM10 levels.",Delhi 938,1175,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Which union territory possesses the second smallest population within the top 2 most polluted union territories, determined by average PM2.5 levels?",Delhi 939,1176,population_based,"Which union territory has the 2nd lowest population among the top 2 most polluted states, based on variance of PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Identify the union territory with the second smallest population among the top 2 most polluted union territories, based on variance of PM2.5 levels.",Delhi 940,1177,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Report the union territory having the largest population among the top 2 most polluted union territories, when pollution is measured by average PM2.5 levels.",Delhi 941,1178,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Determine which union territory has the second smallest population within the top 4 most polluted union territories, based on variance of PM10 levels.",Puducherry 942,1179,population_based,"Which union territory has the lowest population among the top 2 most polluted states, based on total PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].sum().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Which union territory possesses the smallest population among the top 2 most polluted union territories, determined by total PM10 levels?",Chandigarh 943,1180,population_based,"Which union territory has the 2nd highest population among the top 4 most polluted states, based on variance of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].var().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Identify the union territory with the second largest population among the top 4 most polluted union territories, based on variance of PM10 levels.",Jammu and Kashmir 944,1181,population_based,"Which union territory has the 2nd lowest population among the top 4 most polluted states, based on average PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].mean().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second smallest population among the top 4 most polluted union territories, when pollution is measured by average PM2.5 levels.",Puducherry 945,1182,population_based,"Which union territory has the highest population among the top 2 most polluted states, based on 25th percentile of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(2)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[0]['state'] print(max_population_state) true_code() ","Determine which union territory has the largest population within the top 2 most polluted union territories, based on 25th percentile of PM10 levels.",Delhi 946,1184,population_based,"Which union territory has the lowest population among the top 4 most polluted states, based on median PM 2.5 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM2.5'].median().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM2.5', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population').iloc[0]['state'] print(max_population_state) true_code() ","Identify the union territory with the smallest population among the top 4 most polluted union territories, based on median PM2.5 levels.",Chandigarh 947,1185,population_based,"Which union territory has the 2nd highest population among the top 4 most polluted states, based on standard deviation of PM 10 levels?"," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_pm_avg = main_data.groupby('state')['PM10'].std().reset_index() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] merged_df = state_pm_avg.merge(filtered_states_data, on='state', how='inner') merged_df = merged_df.sort_values('PM10', ascending=False) top_polluted_states = merged_df.head(4)['state'].tolist() top_states_population = merged_df[merged_df['state'].isin(top_polluted_states)] max_population_state = top_states_population.sort_values('population', ascending=False).iloc[1]['state'] print(max_population_state) true_code() ","Report the union territory having the second largest population among the top 4 most polluted union territories, when pollution is measured by standard deviation of PM10 levels.",Jammu and Kashmir 948,1187,population_based,What percentage of the population lives in states where the average PM 2.5 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist() total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum() total_population = states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Report what proportion of the population lives in states with average PM2.5 levels exceeding 100.,1.3899548758936036 949,1189,population_based,What percentage of the population lives in states where the 75th percentile of PM 10 exceeds 60?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM10'].quantile(0.75).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 60]['state'].tolist() total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum() total_population = states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Which percentage of the population inhabits states where the 75th percentile of PM10 levels is greater than 60?,99.50963721537744 950,1190,population_based,What percentage of the population lives in states where the 75th percentile of PM 2.5 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist() total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum() total_population = states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Determine the population percentage in states where the 75th percentile of PM2.5 concentration exceeds 100.,10.008852266898977 951,1192,population_based,What percentage of the population lives in states where the 25th percentile of PM 2.5 exceeds 60?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 60]['state'].tolist() total_hazardous_pop = states_data[states_data['state'].isin(hazardous_states)]['population'].sum() total_population = states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Identify what percentage of people reside in states where the 25th percentile of PM2.5 concentration surpasses 60.,0.0 952,1193,population_based,What percentage of the population lives in union territories where the average PM 2.5 exceeds 60?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 60]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Which percentage of the population lives in union territories where the average PM2.5 concentration is greater than 60?,53.53574751447587 953,1194,population_based,What percentage of the population lives in union territories where the average PM 2.5 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].mean().reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Determine the population percentage in union territories where average PM2.5 levels exceed 100.,53.53574751447587 954,1195,population_based,What percentage of the population lives in union territories where the median PM 2.5 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].median().reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Report the proportion of the population dwelling in union territories with median PM2.5 concentrations above 100.,0.0 955,1197,population_based,What percentage of the population lives in union territories where the 75th percentile of PM 2.5 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.75).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 100]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Determine the percentage of people living in union territories where the 75th percentile for PM2.5 is over 100.,53.53574751447587 956,1198,population_based,What percentage of the population lives in union territories where the 25th percentile of PM 10 exceeds 100?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM10'].quantile(0.25).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM10'] > 100]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Report what proportion of the population resides in union territories with a 25th percentile PM10 level greater than 100.,53.53574751447587 957,1199,population_based,What percentage of the population lives in union territories where the 25th percentile of PM 2.5 exceeds 60?," def true_code(): import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") states_data = pd.read_pickle(""preprocessed/states_data.pkl"") ncap_funding_data = pd.read_pickle(""preprocessed/ncap_funding_data.pkl"") state_avg_pm25 = main_data.groupby('state')['PM2.5'].quantile(0.25).reset_index() hazardous_states = state_avg_pm25[state_avg_pm25['PM2.5'] > 60]['state'].tolist() filtered_states_data = states_data[states_data['isUnionTerritory'] == True][['state', 'population']] total_hazardous_pop = filtered_states_data[filtered_states_data['state'].isin(hazardous_states)]['population'].sum() total_population = filtered_states_data['population'].sum() percentage = (total_hazardous_pop / total_population) * 100 print(percentage) true_code() ",Identify the percentage of the population inhabiting union territories where the 25th percentile of PM2.5 surpasses 60.,0.0 958,1200,spatial_aggregation,Which state has the highest average PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 concentration for May 2023.,Delhi 959,1201,spatial_aggregation,Which station has the lowest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Name the station that registered the minimum average PM2.5 level in August 2018.,"Tirumala, Tirupati - APPCB" 960,1202,spatial_aggregation,Which state has the highest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state exhibited the highest 25th percentile for PM10 during August 2019?,Uttar Pradesh 961,1203,spatial_aggregation,Which city has the 2nd highest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that ranked second for the highest median PM10 in September 2018.,Gurugram 962,1204,spatial_aggregation,Which city has the lowest average PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM2.5 value in April 2020?,Eloor 963,1205,spatial_aggregation,Which city has the lowest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Find the city with the lowest mean PM10 reading for September 2018.,Talcher 964,1206,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",What city holds the position of third-lowest for the 25th percentile of PM2.5 in January 2024?,Mandikhera 965,1207,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the third-smallest median PM2.5 figure in November 2022.,Satna 966,1209,spatial_aggregation,Which city has the 2nd highest median PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Name the city that was second in terms of highest median PM2.5 for September 2018.,Patna 967,1210,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest 75th percentile for PM2.5 in February 2023?,"IESD Banaras Hindu University, Varanasi - UPPCB" 968,1211,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the third-lowest median PM2.5 concentration in January 2019.,Tamil Nadu 969,1213,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the third-highest 75th percentile of PM2.5 in February 2018?,Talcher 970,1214,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the second-lowest average PM2.5 reading for July 2023.,Sikkim 971,1215,spatial_aggregation,Which station has the lowest average PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Name the station with the absolute lowest average PM2.5 in November 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 972,1216,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 25th percentile for PM2.5 in April 2022?,"Zero Point GICI, Gangtok - SSPCB" 973,1218,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Find the station with the second-lowest 25th percentile of PM2.5 for October 2018.,"BTM Layout, Bengaluru - CPCB" 974,1220,spatial_aggregation,Which state has the 2nd lowest median PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Name the state with the second-most minimal median PM10 in April 2024.,Jammu and Kashmir 975,1221,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that ranks third for the highest 25th percentile of PM2.5 in November 2020.,Bulandshahr 976,1222,spatial_aggregation,Which station has the highest median PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest median PM10 value in June 2018?,"RIICO Ind. Area III, Bhiwadi - RSPCB" 977,1224,spatial_aggregation,Which city has the 2nd highest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Find the city that was second in terms of highest average PM2.5 for January 2019.,Muzaffarpur 978,1225,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the third-lowest 75th percentile for PM2.5 in November 2022?,"Civic Center, Bhilai - Bhilai Steel Plant" 979,1227,spatial_aggregation,Which station has the 2nd highest average PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second-highest average PM10 in June 2019.,"Dwarka-Sector 8, Delhi - DPCC" 980,1228,spatial_aggregation,Which state has the 3rd highest median PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state exhibited the third-highest median PM2.5 during April 2024?,Odisha 981,1230,spatial_aggregation,Which state has the 3rd highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Find the state that had the third-highest median PM2.5 in December 2021.,Haryana 982,1232,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest 25th percentile of PM10 for June 2024.,"Anand Vihar, Delhi - DPCC" 983,1234,spatial_aggregation,Which city has the 3rd highest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the third-highest mean PM10 concentration in February 2020?,Greater Noida 984,1235,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest 75th percentile for PM10 in March 2019.,Assam 985,1236,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Find the city that ranked second for the highest 25th percentile of PM10 in March 2023.,Begusarai 986,1237,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the minimum 25th percentile for PM2.5 in September 2022?,Mizoram 987,1238,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Name the city with the lowest 25th percentile for PM2.5 in May 2022.,Aizawl 988,1239,spatial_aggregation,Which station has the 2nd highest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second-highest average PM2.5 in January 2023.,"Central Academy for SFS, Byrnihat - PCBA" 989,1240,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the third-highest 75th percentile for PM10 in September 2020?,"Mundka, Delhi - DPCC" 990,1241,spatial_aggregation,Which city has the 2nd lowest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the second-most minimal median PM10 in November 2018.,Thiruvananthapuram 991,1242,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Find the state with the third-lowest average PM2.5 reading for June 2021.,Nagaland 992,1244,spatial_aggregation,Which state has the highest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Name the state exhibiting the highest 75th percentile of PM10 in February 2021.,Uttar Pradesh 993,1246,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the minimum 75th percentile for PM2.5 in December 2020?,Mizoram 994,1248,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Find the station that had the third-lowest 75th percentile for PM2.5 in March 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 995,1249,spatial_aggregation,Which city has the 2nd lowest average PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest mean PM10 reading for November 2018.,Chikkaballapur 996,1250,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Name the station with the third-highest 25th percentile of PM2.5 for March 2022.,"Mirchaibari, Katihar - BSPCB" 997,1252,spatial_aggregation,Which station has the highest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest average PM2.5 value in December 2019?,"Nehru Nagar, Delhi - DPCC" 998,1253,spatial_aggregation,Which station has the 2nd lowest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the second-lowest average PM10 reading for July 2022.,"Sahilara, Maihar - KJS Cements" 999,1254,spatial_aggregation,Which city has the 3rd lowest average PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Find the city with the third-lowest mean PM10 concentration in July 2018.,Thiruvananthapuram 1000,1255,spatial_aggregation,Which state has the 3rd highest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state exhibited the third-highest median PM10 during November 2023?,Bihar 1001,1256,spatial_aggregation,Which station has the 3rd highest median PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Name the station showing the third-highest median PM10 for February 2024.,"Old City, Sri Ganganagar - RSPCB" 1002,1257,spatial_aggregation,Which station has the highest median PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM2.5 value in February 2019.,"Rabindra Bharati University, Kolkata - WBPCB" 1003,1258,spatial_aggregation,Which city has the 3rd highest median PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the third-highest median PM2.5 concentration in April 2023?,Dhanbad 1004,1259,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the second-highest 25th percentile for PM2.5 in January 2021.,"Muzaffarpur Collectorate, Muzaffarpur - BSPCB" 1005,1260,spatial_aggregation,Which station has the 2nd highest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Find the station that registered the second-highest average PM10 in February 2023.,"Central Academy for SFS, Byrnihat - PCBA" 1006,1262,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Name the city with the third-lowest 75th percentile for PM10 in March 2021.,Eloor 1007,1263,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the third-most minimal 25th percentile of PM10 in October 2021.,Udupi 1008,1264,spatial_aggregation,Which station has the 2nd highest average PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest average PM10 in January 2022?,"Mayaganj, Bhagalpur - BSPCB" 1009,1266,spatial_aggregation,Which city has the 2nd highest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Find the city that was second in terms of highest average PM10 for July 2022.,Saharsa 1010,1267,spatial_aggregation,Which state has the highest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state exhibited the highest average PM2.5 during November 2024?,Delhi 1011,1271,spatial_aggregation,Which city has the 3rd highest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that ranks third for the highest median PM2.5 in July 2020.,Jodhpur 1012,1272,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Find the station with the third-lowest 75th percentile for PM2.5 in March 2024.,"Deen Dayal Nagar, Sagar - MPPCB" 1013,1273,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest 25th percentile PM10 value in February 2021?,Shillong 1014,1274,spatial_aggregation,Which station has the 2nd highest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest average PM10 for November 2024.,"Mundka, Delhi - DPCC" 1015,1275,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the second-lowest 75th percentile for PM2.5 in December 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1016,1276,spatial_aggregation,Which station has the 2nd highest median PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest median PM10 in September 2020?,"Knowledge Park - V, Greater Noida - UPPCB" 1017,1277,spatial_aggregation,Which state has the highest median PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the peak median PM2.5 concentration for June 2020.,Delhi 1018,1278,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Find the station that had the third-lowest 75th percentile for PM10 in June 2020.,"Karve Road, Pune - MPCB" 1019,1281,spatial_aggregation,Which station has the 2nd lowest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the second-lowest average PM10 reading for November 2021.,"Brahmagiri, Udupi - KSPCB" 1020,1282,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-smallest median PM2.5 figure in July 2022?,Manipur 1021,1283,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the third-highest 25th percentile of PM2.5 for August 2021.,"Anand Vihar, Delhi - DPCC" 1022,1284,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Find the state with the highest 25th percentile for PM2.5 in August 2023.,Jharkhand 1023,1285,spatial_aggregation,Which state has the 3rd highest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the third-highest mean PM2.5 concentration in April 2019?,Haryana 1024,1286,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest average PM2.5 in December 2019.,"Bandhavgar Colony, Satna - Birla Cement" 1025,1287,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-most minimal median PM2.5 in May 2022.,Imphal 1026,1288,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest 25th percentile for PM2.5 in March 2021?,Shillong 1027,1289,spatial_aggregation,Which state has the 2nd highest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that was second in terms of highest average PM2.5 for February 2024.,Delhi 1028,1291,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state exhibited the second-highest 25th percentile for PM2.5 during July 2020?,Haryana 1029,1292,spatial_aggregation,Which city has the highest 25th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Name the city showing the highest 25th percentile of PM10 for March 2024.,Sri Ganganagar 1030,1294,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest 25th percentile for PM10 in May 2018?,"Sanegurava Halli, Bengaluru - KSPCB" 1031,1297,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile for PM10 in July 2024?,Chengalpattu 1032,1298,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest 75th percentile for PM10 in September 2018.,"Hombegowda Nagar, Bengaluru - KSPCB" 1033,1301,spatial_aggregation,Which city has the highest average PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest average PM2.5 concentration for September 2018.,Kolar 1034,1302,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Find the city that ranked second for the highest 75th percentile of PM2.5 in June 2022.,Kurukshetra 1035,1303,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the third-lowest 75th percentile for PM10 in October 2019?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1036,1304,spatial_aggregation,Which city has the highest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Name the city showing the highest median PM2.5 for February 2022.,Munger 1037,1305,spatial_aggregation,Which station has the 3rd lowest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the third-lowest average PM10 reading for February 2023.,"Sector-3B Avas Vikas Colony, Agra - UPPCB" 1038,1307,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that ranks third for the highest 75th percentile of PM2.5 in August 2023.,Panipat 1039,1308,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Find the city with the second-lowest mean PM2.5 concentration in July 2018.,Tirupati 1040,1310,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Name the city with the second-lowest mean PM2.5 reading for September 2019.,Eloor 1041,1311,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile for PM2.5 in August 2023.,Mizoram 1042,1312,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state exhibited the third-most minimal 25th percentile of PM10 in April 2019?,Kerala 1043,1313,spatial_aggregation,Which station has the highest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest average PM2.5 value in December 2020.,"Jahangirpuri, Delhi - DPCC" 1044,1314,spatial_aggregation,Which city has the 2nd highest average PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Find the city that was second in terms of highest average PM2.5 for May 2022.,Rohtak 1045,1315,spatial_aggregation,Which state has the 2nd lowest median PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the second-most minimal median PM10 in October 2023?,Manipur 1046,1316,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Name the station with the third-highest 25th percentile of PM2.5 for March 2021.,"Bawana, Delhi - DPCC" 1047,1317,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the third-lowest 25th percentile for PM2.5 in July 2020.,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 1048,1318,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the second-lowest 75th percentile for PM2.5 in July 2021?,Koppal 1049,1319,spatial_aggregation,Which city has the highest 75th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city showing the highest 75th percentile of PM10 for October 2023.,Hanumangarh 1050,1321,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile for PM10 in March 2022?,Udupi 1051,1323,spatial_aggregation,Which city has the 3rd lowest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the third-lowest mean PM10 concentration in November 2024.,Aizawl 1052,1324,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state exhibited the third-lowest 75th percentile for PM2.5 in April 2022?,Sikkim 1053,1325,spatial_aggregation,Which station has the 2nd highest average PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that registered the second-highest average PM10 in June 2020.,"GM Office, Brajrajnagar - OSPCB" 1054,1326,spatial_aggregation,Which state has the 3rd highest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Find the state with the third-highest mean PM10 concentration in December 2024.,Tripura 1055,1327,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-most minimal 25th percentile of PM10 in June 2019?,Telangana 1056,1328,spatial_aggregation,Which city has the lowest average PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Name the city with the lowest mean PM10 reading for April 2021.,Kolar 1057,1329,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the third-lowest average PM2.5 value in February 2023.,Mandikhera 1058,1330,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest 25th percentile of PM2.5 for June 2020?,"ITO, Delhi - CPCB" 1059,1331,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the third-highest 75th percentile for PM2.5 in September 2020.,"Talkatora District Industries Center, Lucknow - CPCB" 1060,1333,spatial_aggregation,Which station has the highest median PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station exhibited the highest median PM10 during December 2023?,"Samanpura, Patna - BSPCB" 1061,1336,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the third-highest 75th percentile for PM10 in December 2023?,"Wazirpur, Delhi - DPCC" 1062,1337,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the second-lowest 75th percentile for PM2.5 in November 2024.,Manipur 1063,1338,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Find the station with the third-highest 25th percentile of PM10 for December 2022.,"DRCC Anandpur, Begusarai - BSPCB" 1064,1340,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Name the city with the second-lowest 75th percentile for PM10 in January 2019.,Vijayawada 1065,1341,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the third-most minimal 25th percentile of PM2.5 in March 2020.,"Hardev Nagar, Bathinda - PPCB" 1066,1342,spatial_aggregation,Which city has the 3rd highest average PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the third-highest mean PM2.5 concentration in March 2021?,Siliguri 1067,1343,spatial_aggregation,Which station has the highest average PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest average PM2.5 value in March 2023.,"Central Academy for SFS, Byrnihat - PCBA" 1068,1345,spatial_aggregation,Which station has the highest 25th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station exhibited the highest 25th percentile for PM10 during December 2021?,"Anand Vihar, Delhi - DPCC" 1069,1346,spatial_aggregation,Which city has the highest 75th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Name the city showing the highest 75th percentile of PM10 for December 2018.,Noida 1070,1347,spatial_aggregation,Which state has the 3rd lowest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the third-lowest mean PM10 concentration in December 2024.,Karnataka 1071,1348,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the third-highest 75th percentile for PM10 in September 2024?,"Mundka, Delhi - DPCC" 1072,1353,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the second-lowest 75th percentile for PM2.5 in March 2021.,Chandigarh 1073,1354,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state exhibited the third-highest 25th percentile for PM2.5 during April 2019?,Haryana 1074,1355,spatial_aggregation,Which station has the 2nd highest average PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that registered the second-highest average PM2.5 in September 2021.,"SVPI Airport Hansol, Ahmedabad - IITM" 1075,1356,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Find the city with the second-lowest 75th percentile for PM2.5 in February 2020.,Mysuru 1076,1358,spatial_aggregation,Which state has the highest median PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Name the state with the highest median PM10 value in April 2020.,Uttar Pradesh 1077,1359,spatial_aggregation,Which station has the lowest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the minimum median PM10 reading for September 2022.,"Brahmagiri, Udupi - KSPCB" 1078,1360,spatial_aggregation,Which state has the lowest median PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest median PM2.5 figure in June 2020?,Mizoram 1079,1361,spatial_aggregation,Which state has the 3rd highest median PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest median PM10 concentration in January 2022.,Tripura 1080,1362,spatial_aggregation,Which station has the highest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Find the station showing the highest 25th percentile of PM10 for July 2018.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 1081,1363,spatial_aggregation,Which city has the 2nd highest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest average PM2.5 for December 2020?,Bulandshahr 1082,1364,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Name the station with the third-highest 25th percentile of PM10 for April 2019.,"Anand Vihar, Delhi - DPCC" 1083,1365,spatial_aggregation,Which city has the 3rd highest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that ranks third for the highest median PM2.5 in March 2023.,Bettiah 1084,1367,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the second-most minimal 25th percentile of PM10 in May 2024.,Palkalaiperur 1085,1369,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state exhibited the second-lowest 75th percentile for PM2.5 during September 2019?,Kerala 1086,1370,spatial_aggregation,Which station has the highest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Name the station with the highest median PM10 value in August 2024.,"Central Academy for SFS, Byrnihat - PCBA" 1087,1371,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the third-most minimal 25th percentile of PM10 in October 2023.,Arunachal Pradesh 1088,1372,spatial_aggregation,Which station has the highest median PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest median PM10 value in September 2019?,"Sirifort, Delhi - CPCB" 1089,1373,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 75th percentile for PM2.5 in May 2021.,Mizoram 1090,1374,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Find the state with the third-lowest median PM2.5 concentration in February 2024.,Telangana 1091,1376,spatial_aggregation,Which state has the highest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Name the state with the highest average PM10 concentration for January 2024.,Delhi 1092,1378,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the third-highest 75th percentile of PM2.5 in February 2021?,Noida 1093,1380,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Find the state with the third-lowest average PM2.5 reading for November 2023.,Puducherry 1094,1381,spatial_aggregation,Which city has the lowest average PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM2.5 value in November 2023?,Aizawl 1095,1382,spatial_aggregation,Which city has the 3rd highest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Name the city that had the third-highest median PM2.5 in September 2020.,Charkhi Dadri 1096,1383,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the third-lowest 75th percentile for PM2.5 in February 2020.,Karnataka 1097,1385,spatial_aggregation,Which city has the highest 75th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city showing the highest 75th percentile of PM10 for April 2021.,Baghpat 1098,1386,spatial_aggregation,Which city has the 3rd highest average PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Find the city with the third-highest mean PM2.5 concentration in January 2024.,Saharsa 1099,1387,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the third-highest 75th percentile for PM2.5 in April 2021?,"Mundka, Delhi - DPCC" 1100,1388,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest 25th percentile of PM10 for July 2024.,"Old City, Sri Ganganagar - RSPCB" 1101,1389,spatial_aggregation,Which station has the 3rd highest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the third-highest median PM2.5 concentration in July 2019.,"Gobind Pura, Yamuna Nagar - HSPCB" 1102,1390,spatial_aggregation,Which station has the highest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest average PM10 value in October 2021?,"Anand Vihar, Delhi - DPCC" 1103,1393,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the second-minimum 25th percentile for PM2.5 in July 2024?,"DM College of Science, Imphal - Manipur PCB" 1104,1394,spatial_aggregation,Which station has the 3rd highest median PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Name the station with the third-highest median PM2.5 concentration in January 2021.,"Nehru Nagar, Delhi - DPCC" 1105,1397,spatial_aggregation,Which city has the highest average PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest average PM10 concentration for October 2023.,Greater Noida 1106,1398,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Find the station that had the third-lowest 75th percentile for PM10 in January 2020.,"Sanegurava Halli, Bengaluru - KSPCB" 1107,1400,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Name the station with the highest 25th percentile for PM2.5 in November 2022.,"Gandak Colony, Motihari - BSPCB" 1108,1401,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the minimum 25th percentile for PM2.5 in May 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1109,1402,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the second-highest 75th percentile of PM10 for March 2024?,Assam 1110,1404,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Find the station with the second-lowest median PM2.5 in July 2020.,"Borivali East, Mumbai - MPCB" 1111,1406,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Name the city with the second-lowest mean PM2.5 concentration in December 2021.,Aizawl 1112,1407,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the third-highest 75th percentile for PM10 in March 2020.,Delhi 1113,1408,spatial_aggregation,Which state has the 2nd highest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state was second in terms of highest average PM2.5 for July 2022?,Haryana 1114,1409,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile PM2.5 value in January 2022.,Delhi 1115,1411,spatial_aggregation,Which city has the highest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM10 in March 2021?,Guwahati 1116,1412,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest 75th percentile for PM2.5 in May 2024.,"IESD Banaras Hindu University, Varanasi - UPPCB" 1117,1413,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that ranks third for the highest 75th percentile of PM10 in February 2019.,Bhiwadi 1118,1415,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the second-lowest 25th percentile for PM2.5 in December 2020.,Shillong 1119,1416,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Find the station with the third-lowest average PM2.5 in January 2024.,"General Hospital, Mandikhera - HSPCB" 1120,1417,spatial_aggregation,Which station has the 2nd highest average PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the second-highest average PM10 in March 2018?,"Anand Vihar, Delhi - DPCC" 1121,1418,spatial_aggregation,Which state has the 2nd highest median PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Name the state with the second-highest median PM2.5 concentration in October 2021.,Uttar Pradesh 1122,1420,spatial_aggregation,Which state has the 2nd highest average PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state was second in terms of highest average PM2.5 for June 2018?,Delhi 1123,1421,spatial_aggregation,Which station has the 3rd highest median PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the third-highest median PM10 concentration in June 2018.,"Sirifort, Delhi - CPCB" 1124,1422,spatial_aggregation,Which city has the 2nd lowest average PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Find the city with the second-lowest mean PM10 reading for June 2024.,Gangtok 1125,1423,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-smallest median PM2.5 figure in May 2022?,Sikkim 1126,1424,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Name the state with the lowest 25th percentile for PM2.5 in March 2022.,Mizoram 1127,1425,spatial_aggregation,Which state has the 3rd highest median PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the third-highest median PM2.5 during February 2023.,Tripura 1128,1426,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second-minimum 25th percentile for PM2.5 in December 2022?,"Deen Dayal Nagar, Sagar - MPPCB" 1129,1429,spatial_aggregation,Which city has the lowest median PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest median PM10 value in January 2018?,Bengaluru 1130,1431,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the second-most minimal 75th percentile of PM10 in August 2024.,Meghalaya 1131,1432,spatial_aggregation,Which station has the 3rd highest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the third-highest median PM2.5 in August 2020?,"Nathu Colony, Ballabgarh - HSPCB" 1132,1434,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Find the station with the third-lowest 75th percentile for PM10 in August 2024.,"Diwator Nagar, Koppal - KSPCB" 1133,1435,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest 75th percentile for PM10 in August 2019?,Bathinda 1134,1436,spatial_aggregation,Which state has the lowest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Name the state with the lowest average PM10 reading for December 2024.,Meghalaya 1135,1437,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-lowest 75th percentile for PM10 in December 2024.,Koppal 1136,1441,spatial_aggregation,Which state has the 3rd lowest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-lowest average PM10 reading for September 2023?,Mizoram 1137,1442,spatial_aggregation,Which station has the 2nd highest median PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest median PM10 for August 2020.,"Nathu Colony, Ballabgarh - HSPCB" 1138,1443,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that ranks third for the highest 25th percentile of PM10 in November 2024.,Tonk 1139,1444,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest 75th percentile for PM10 in June 2023?,Nandesari 1140,1445,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the third-lowest median PM2.5 in July 2023.,"Zero Point GICI, Gangtok - SSPCB" 1141,1446,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Find the station with the third-lowest median PM2.5 in April 2018.,"Secretariat, Amaravati - APPCB" 1142,1447,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest 25th percentile for PM2.5 in April 2022?,"Secretariat, Amaravati - APPCB" 1143,1449,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that ranked second for the highest 25th percentile of PM10 in December 2018.,Jorapokhar 1144,1450,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the third-smallest 25th percentile PM2.5 figure in October 2023?,Manguraha 1145,1453,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest 25th percentile for PM2.5 in August 2020?,Kalaburagi 1146,1455,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile for PM10 in August 2024.,Sikkim 1147,1456,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest 75th percentile for PM10 in December 2024?,"Samanpura, Patna - BSPCB" 1148,1457,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the third-most minimal 25th percentile of PM2.5 in April 2018.,Tamil Nadu 1149,1458,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Find the state with the second-lowest 25th percentile for PM2.5 in February 2024.,Manipur 1150,1459,spatial_aggregation,Which city has the 2nd highest average PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest average PM10 for September 2019?,Bhiwadi 1151,1460,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Name the state with the third-highest 75th percentile for PM10 in May 2021.,Haryana 1152,1461,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the second-lowest 75th percentile for PM2.5 in April 2019.,"PWD Grounds, Vijayawada - APPCB" 1153,1462,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state exhibited the third-most minimal 25th percentile of PM10 in July 2018?,Kerala 1154,1463,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest 25th percentile for PM10 during October 2023.,Chandigarh 1155,1464,spatial_aggregation,Which station has the 2nd highest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Find the station that registered the second-highest average PM2.5 in May 2019.,"North Campus, DU, Delhi - IMD" 1156,1465,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest 25th percentile for PM2.5 in May 2021?,"Devaraj Urs Badavane, Davanagere - KSPCB" 1157,1466,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Name the state with the lowest 75th percentile for PM2.5 in December 2018.,Kerala 1158,1467,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile for PM10 in June 2021.,Mizoram 1159,1468,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 25th percentile for PM10 in October 2023?,"Zero Point GICI, Gangtok - SSPCB" 1160,1469,spatial_aggregation,Which city has the lowest average PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest mean PM10 concentration in March 2020.,Amaravati 1161,1470,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Find the station with the absolute lowest 25th percentile of PM10 in December 2024.,"Airport Area, Indore - IMC" 1162,1471,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest 75th percentile for PM10 in December 2023?,"Muradpur, Patna - BSPCB" 1163,1472,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Name the state with the third-lowest average PM2.5 reading for December 2023.,Jammu and Kashmir 1164,1473,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station showing the second-highest 25th percentile of PM2.5 for September 2024.,"GIDC, Nandesari - Nandesari Ind. Association" 1165,1475,spatial_aggregation,Which state has the 2nd lowest median PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the second-most minimal median PM10 in December 2023.,Sikkim 1166,1476,spatial_aggregation,Which station has the lowest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Find the station with the minimum median PM10 reading for February 2023.,"Brahmagiri, Udupi - KSPCB" 1167,1477,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-lowest average PM2.5 reading for May 2018?,West Bengal 1168,1478,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Name the state with the lowest 75th percentile for PM2.5 in September 2020.,Mizoram 1169,1480,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state exhibited the third-highest 75th percentile for PM10 during November 2020?,Haryana 1170,1481,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the third-lowest median PM2.5 concentration in December 2020.,Karnataka 1171,1482,spatial_aggregation,Which city has the lowest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Find the city with the lowest median PM2.5 figure in June 2022.,Koppal 1172,1483,spatial_aggregation,Which city has the 2nd highest median PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest median PM10 for August 2023?,Hisar 1173,1484,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Name the state with the second-highest 75th percentile for PM2.5 in May 2024.,Haryana 1174,1485,spatial_aggregation,Which city has the 2nd highest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that ranked second for the highest median PM10 in April 2023.,Byrnihat 1175,1486,spatial_aggregation,Which station has the 3rd lowest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest average PM10 in October 2021?,"Stuart Hill, Madikeri - KSPCB" 1176,1488,spatial_aggregation,Which city has the 2nd lowest average PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Find the city with the second-lowest mean PM10 reading for September 2020.,Shillong 1177,1489,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the second-highest 75th percentile of PM2.5 in August 2023?,Kaithal 1178,1492,spatial_aggregation,Which station has the lowest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the minimum average PM2.5 level in July 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1179,1493,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile PM2.5 value in April 2020.,Assam 1180,1494,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Find the station with the third-highest 75th percentile for PM2.5 in March 2020.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 1181,1495,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the second-lowest 75th percentile for PM2.5 in July 2020?,Hubballi 1182,1499,spatial_aggregation,Which city has the highest 25th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city showing the highest 25th percentile of PM10 for January 2024.,Delhi 1183,1500,spatial_aggregation,Which station has the 2nd highest average PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Find the station that registered the second-highest average PM2.5 in August 2024.,"Crescent University, Chengalpattu - TNPCB" 1184,1501,spatial_aggregation,Which state has the highest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state exhibited the highest average PM10 during November 2020?,Delhi 1185,1502,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Name the state with the third-lowest 75th percentile for PM10 in July 2019.,Tamil Nadu 1186,1503,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station showing the second-highest 25th percentile of PM10 for October 2021.,"Burari Crossing, Delhi - IMD" 1187,1504,spatial_aggregation,Which state has the highest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM10 concentration for March 2019?,Uttar Pradesh 1188,1506,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Find the city with the second-lowest 75th percentile for PM10 in December 2022.,Madikeri 1189,1507,spatial_aggregation,Which city has the 3rd lowest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the third-lowest mean PM10 concentration in April 2024?,Puducherry 1190,1510,spatial_aggregation,Which state has the 2nd lowest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the second-lowest average PM10 reading for April 2023?,Sikkim 1191,1511,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that ranks third for the highest 25th percentile of PM10 in December 2020.,Greater Noida 1192,1512,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Find the state with the lowest 75th percentile for PM10 in June 2022.,Sikkim 1193,1513,spatial_aggregation,Which station has the lowest median PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the minimum median PM2.5 level in August 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1194,1514,spatial_aggregation,Which state has the 3rd highest median PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Name the state with the third-highest median PM2.5 concentration in October 2023.,Haryana 1195,1516,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the second-highest 75th percentile of PM10 for May 2022?,Haryana 1196,1517,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest 25th percentile for PM10 in October 2022.,Naharlagun 1197,1519,spatial_aggregation,Which state has the 3rd lowest median PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the third-lowest median PM10 reading for March 2020?,Mizoram 1198,1520,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Name the city that was second in terms of highest 75th percentile for PM10 in November 2020.,Baghpat 1199,1521,spatial_aggregation,Which city has the 2nd lowest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-lowest mean PM10 reading for March 2024.,Varanasi 1200,1523,spatial_aggregation,Which city has the lowest average PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest average PM2.5 value in October 2023.,Silchar 1201,1524,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Find the city with the lowest 25th percentile for PM2.5 in September 2021.,Aizawl 1202,1525,spatial_aggregation,Which station has the 2nd lowest average PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second-lowest average PM10 in September 2022?,"Zero Point GICI, Gangtok - SSPCB" 1203,1526,spatial_aggregation,Which city has the highest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Name the city with the highest average PM2.5 concentration for November 2020.,Ghaziabad 1204,1527,spatial_aggregation,Which station has the lowest average PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the minimum average PM10 reading for March 2023.,"Brahmagiri, Udupi - KSPCB" 1205,1528,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the second-most minimal 25th percentile of PM2.5 in May 2022?,Sikkim 1206,1529,spatial_aggregation,Which city has the 3rd highest average PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that had the third-highest average PM10 in August 2023.,Greater Noida 1207,1530,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Find the station with the lowest 75th percentile for PM10 in November 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1208,1531,spatial_aggregation,Which state has the 3rd highest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the third-highest median PM2.5 concentration in December 2022?,Himachal Pradesh 1209,1532,spatial_aggregation,Which station has the lowest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Name the station with the absolute lowest average PM10 in August 2022.,"Brahmagiri, Udupi - KSPCB" 1210,1533,spatial_aggregation,Which station has the 3rd highest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the third-highest average PM10 in May 2018.,"Rohini, Delhi - DPCC" 1211,1534,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest median PM2.5 in February 2024?,"Bhelupur, Varanasi - UPPCB" 1212,1535,spatial_aggregation,Which city has the 2nd highest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that ranked second for the highest median PM2.5 in April 2019.,Bhiwadi 1213,1536,spatial_aggregation,Which city has the lowest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Find the city with the lowest mean PM10 reading for April 2024.,Chengalpattu 1214,1537,spatial_aggregation,Which state has the highest median PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state exhibited the highest median PM2.5 during April 2022?,Delhi 1215,1538,spatial_aggregation,Which station has the 2nd highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest median PM10 for July 2022.,"Police Line, Saharsa - BSPCB" 1216,1539,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile for PM10 in November 2022.,Udupi 1217,1540,spatial_aggregation,Which state has the 2nd highest average PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state was second in terms of highest average PM10 for August 2019?,Uttar Pradesh 1218,1541,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the third-highest 25th percentile of PM10 for October 2020.,"Loni, Ghaziabad - UPPCB" 1219,1542,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Find the station with the second-minimum 25th percentile for PM2.5 in August 2024.,"Manipur University, Imphal - Manipur PCB" 1220,1543,spatial_aggregation,Which station has the 3rd lowest median PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest median PM10 in January 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1221,1544,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Name the city with the second-lowest 75th percentile for PM2.5 in December 2023.,Aizawl 1222,1545,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the third-highest 75th percentile for PM2.5 in December 2022.,Himachal Pradesh 1223,1546,spatial_aggregation,Which state has the lowest median PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest median PM2.5 figure in April 2023?,Mizoram 1224,1547,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the second-highest 75th percentile for PM10 in December 2022.,"DRCC Anandpur, Begusarai - BSPCB" 1225,1548,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Find the city that ranked second for the highest 75th percentile of PM2.5 in October 2019.,Ghaziabad 1226,1549,spatial_aggregation,Which city has the 3rd lowest average PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the third-lowest mean PM10 concentration in June 2021?,Koppal 1227,1552,spatial_aggregation,Which city has the lowest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM2.5 value in February 2020?,Satna 1228,1553,spatial_aggregation,Which station has the lowest average PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum average PM2.5 level in November 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1229,1555,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the third-lowest average PM2.5 in August 2023?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1230,1556,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Name the state with the third-lowest median PM2.5 concentration in August 2020.,Maharashtra 1231,1557,spatial_aggregation,Which city has the 2nd lowest average PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-lowest mean PM10 reading for December 2018.,Vijayawada 1232,1558,spatial_aggregation,Which station has the highest median PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station exhibited the highest median PM2.5 during June 2024?,"Chandni Chowk, Delhi - IITM" 1233,1559,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the third-lowest median PM2.5 concentration in February 2021.,Mizoram 1234,1560,spatial_aggregation,Which city has the highest median PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Find the city with the highest median PM10 value in March 2024.,Byrnihat 1235,1561,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second-lowest 75th percentile for PM2.5 in October 2020?,"Stuart Hill, Madikeri - KSPCB" 1236,1562,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Name the state with the third-lowest 75th percentile for PM10 in May 2019.,Assam 1237,1563,spatial_aggregation,Which station has the 2nd highest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second-highest average PM2.5 in November 2018.,"Jahangirpuri, Delhi - DPCC" 1238,1564,spatial_aggregation,Which state has the highest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM10 concentration for December 2024?,Jharkhand 1239,1565,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum 25th percentile for PM2.5 in August 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1240,1568,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest 25th percentile for PM2.5 in May 2022.,"DM College of Science, Imphal - Manipur PCB" 1241,1569,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-lowest 75th percentile for PM10 in March 2024.,Varanasi 1242,1570,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city ranks third for the highest 25th percentile of PM10 in October 2024?,Ghaziabad 1243,1572,spatial_aggregation,Which city has the 2nd highest median PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Find the city that ranked second for the highest median PM2.5 in September 2023.,Surat 1244,1574,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest average PM2.5 in May 2022.,"DM College of Science, Imphal - Manipur PCB" 1245,1575,spatial_aggregation,Which station has the lowest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the minimum average PM2.5 level in May 2024.,"Crescent University, Chengalpattu - TNPCB" 1246,1577,spatial_aggregation,Which city has the highest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM2.5 concentration for August 2020.,Nandesari 1247,1579,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the third-highest 25th percentile for PM2.5 in February 2021?,"Mundka, Delhi - DPCC" 1248,1580,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Name the state with the second-highest 25th percentile for PM10 in June 2022.,Delhi 1249,1581,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile PM2.5 value in May 2019.,Delhi 1250,1582,spatial_aggregation,Which station has the 2nd highest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest median PM2.5 in August 2024?,"Central Academy for SFS, Byrnihat - PCBA" 1251,1583,spatial_aggregation,Which state has the 3rd highest average PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest mean PM10 concentration in March 2020.,Uttar Pradesh 1252,1584,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Find the state with the lowest 75th percentile for PM10 in November 2022.,Mizoram 1253,1585,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-most minimal 25th percentile of PM2.5 in February 2020?,Karnataka 1254,1586,spatial_aggregation,Which station has the 2nd lowest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Name the station with the second-lowest median PM10 in April 2019.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 1255,1588,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the second-most minimal 25th percentile of PM10 in July 2022?,Odisha 1256,1589,spatial_aggregation,Which station has the lowest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum average PM10 reading for February 2024.,"Manipur University, Imphal - Manipur PCB" 1257,1590,spatial_aggregation,Which station has the 2nd highest average PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Find the station that registered the second-highest average PM10 in January 2018.,"Vasundhara, Ghaziabad - UPPCB" 1258,1591,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the third-lowest average PM2.5 in December 2020?,"Bandhavgar Colony, Satna - Birla Cement" 1259,1592,spatial_aggregation,Which state has the 2nd highest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Name the state with the second-highest median PM2.5 concentration in January 2018.,Bihar 1260,1594,spatial_aggregation,Which state has the 3rd lowest median PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-lowest median PM10 reading for January 2019?,Karnataka 1261,1595,spatial_aggregation,Which city has the highest 25th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city showing the highest 25th percentile of PM10 for February 2018.,Pune 1262,1596,spatial_aggregation,Which station has the highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Find the station with the highest median PM10 value in February 2023.,"Housing Board, Hanumangarh - RSPCB" 1263,1597,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third-lowest 25th percentile for PM2.5 in May 2024?,"Kasturi Nagar, Bengaluru - KSPCB" 1264,1598,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Name the station with the second-lowest median PM2.5 in October 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1265,1599,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile for PM2.5 in January 2021.,Meghalaya 1266,1600,spatial_aggregation,Which station has the lowest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the minimum average PM2.5 level in December 2021?,"Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 1267,1601,spatial_aggregation,Which state has the 2nd highest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that was second in terms of highest average PM10 for March 2019.,Odisha 1268,1602,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Find the city with the third-lowest 75th percentile for PM10 in January 2023.,Srinagar 1269,1603,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 25th percentile for PM2.5 in November 2018?,"Bandhavgar Colony, Satna - Birla Cement" 1270,1604,spatial_aggregation,Which city has the highest average PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Name the city with the highest average PM2.5 concentration for March 2024.,Byrnihat 1271,1605,spatial_aggregation,Which state has the 2nd highest median PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the second-highest median PM10 during October 2018.,Jharkhand 1272,1606,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the second-lowest average PM2.5 reading for January 2018?,Maharashtra 1273,1607,spatial_aggregation,Which station has the lowest average PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum average PM2.5 level in March 2023.,"SVPI Airport Hansol, Ahmedabad - IITM" 1274,1608,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Find the state with the third-most minimal 25th percentile of PM2.5 in July 2024.,Jharkhand 1275,1609,spatial_aggregation,Which station has the highest average PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest average PM10 value in May 2021?,"Chandni Chowk, Delhi - IITM" 1276,1611,spatial_aggregation,Which city has the highest 75th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city showing the highest 75th percentile of PM10 for November 2019.,Ghaziabad 1277,1612,spatial_aggregation,Which city has the 2nd highest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest average PM2.5 for March 2019?,Bhiwadi 1278,1613,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station showing the second-highest 25th percentile of PM10 for June 2021.,"Murthal, Sonipat - HSPCB" 1279,1617,spatial_aggregation,Which state has the highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile PM10 value in July 2021.,Delhi 1280,1618,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the third-smallest 25th percentile PM2.5 figure in April 2018?,Rajamahendravaram 1281,1619,spatial_aggregation,Which station has the lowest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum average PM2.5 level in February 2023.,"Sector-3B Avas Vikas Colony, Agra - UPPCB" 1282,1620,spatial_aggregation,Which city has the 3rd lowest average PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Find the city with the third-lowest mean PM10 concentration in May 2021.,Gadag 1283,1621,spatial_aggregation,Which state has the 2nd lowest average PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the second-lowest average PM10 reading for September 2024?,Meghalaya 1284,1623,spatial_aggregation,Which city has the lowest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM2.5 figure in November 2022.,Aizawl 1285,1624,spatial_aggregation,Which state has the lowest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest average PM2.5 reading for April 2019?,Andhra Pradesh 1286,1625,spatial_aggregation,Which state has the 3rd highest median PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest median PM10 concentration in October 2023.,Maharashtra 1287,1627,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest mean PM2.5 reading for September 2023?,Koppal 1288,1628,spatial_aggregation,Which station has the lowest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Name the station with the minimum average PM2.5 level in June 2021.,"Ratanpura, Rupnagar - Ambuja Cements" 1289,1630,spatial_aggregation,Which state has the highest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM10 concentration for April 2019?,Uttar Pradesh 1290,1631,spatial_aggregation,Which state has the 3rd lowest median PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the third-lowest median PM10 reading for October 2019.,Tamil Nadu 1291,1632,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Find the state with the lowest 75th percentile for PM10 in May 2022.,Sikkim 1292,1633,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest 75th percentile for PM10 in November 2021?,Faridabad 1293,1634,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Name the state with the second-lowest average PM2.5 reading for October 2021.,Meghalaya 1294,1635,spatial_aggregation,Which city has the 3rd lowest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the third-lowest mean PM10 concentration in July 2023.,Bidar 1295,1636,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 75th percentile for PM2.5 in December 2020?,"Bandra, Mumbai - MPCB" 1296,1637,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the second-lowest 25th percentile for PM10 in April 2022.,"Zero Point GICI, Gangtok - SSPCB" 1297,1639,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM2.5 in January 2018?,Ghaziabad 1298,1640,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Name the city that ranks third for the highest 25th percentile of PM10 in December 2018.,Bulandshahr 1299,1641,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the minimum 25th percentile for PM2.5 in September 2020.,"Sanathnagar, Hyderabad - TSPCB" 1300,1642,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the second-highest 75th percentile of PM10 for February 2018?,Odisha 1301,1644,spatial_aggregation,Which station has the 3rd highest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Find the station with the third-highest average PM10 in February 2023.,"Samanpura, Patna - BSPCB" 1302,1645,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state exhibited the third-lowest 75th percentile for PM10 in June 2018?,Maharashtra 1303,1646,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Name the state with the second-lowest 25th percentile for PM10 in April 2023.,Arunachal Pradesh 1304,1647,spatial_aggregation,Which city has the highest average PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest average PM2.5 concentration for October 2019.,Ghaziabad 1305,1648,spatial_aggregation,Which city has the 3rd highest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the third-highest mean PM10 concentration in January 2024?,Delhi 1306,1649,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the minimum 25th percentile for PM2.5 in December 2023.,"Zero Point GICI, Gangtok - SSPCB" 1307,1651,spatial_aggregation,Which city has the 2nd highest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest average PM10 for January 2024?,Sri Ganganagar 1308,1652,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Name the city with the second-lowest 25th percentile for PM10 in January 2018.,Vijayawada 1309,1653,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that ranks third for the highest 75th percentile of PM10 in February 2023.,Begusarai 1310,1654,spatial_aggregation,Which station has the 2nd lowest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second-lowest median PM10 in November 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1311,1655,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 25th percentile for PM10 in August 2018.,Telangana 1312,1656,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Find the state with the highest 75th percentile for PM2.5 in February 2022.,Delhi 1313,1658,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Name the state with the second-highest 75th percentile for PM2.5 in March 2022.,Bihar 1314,1659,spatial_aggregation,Which city has the 2nd lowest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second-lowest mean PM10 reading for February 2020.,Coimbatore 1315,1660,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest 75th percentile for PM10 in May 2024?,Koppal 1316,1661,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third-highest 25th percentile for PM2.5 during January 2018.,Delhi 1317,1662,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Find the state with the third-most minimal 25th percentile of PM2.5 in October 2023.,Assam 1318,1664,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Name the station with the highest 75th percentile for PM2.5 in June 2022.,"Sector 11, Faridabad - HSPCB" 1319,1666,spatial_aggregation,Which city has the 2nd lowest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-most minimal median PM10 in February 2023?,Nandesari 1320,1667,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the second-lowest 75th percentile for PM10 in October 2022.,"Zero Point GICI, Gangtok - SSPCB" 1321,1669,spatial_aggregation,Which state has the lowest median PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest median PM10 reading for March 2023?,Arunachal Pradesh 1322,1670,spatial_aggregation,Which city has the lowest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Name the city with the lowest median PM2.5 figure in January 2018.,Chandrapur 1323,1673,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the second-highest 75th percentile for PM10 in January 2023.,"Police Line, Saharsa - BSPCB" 1324,1674,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Find the state with the second-lowest 75th percentile for PM10 in November 2020.,Mizoram 1325,1675,spatial_aggregation,Which city has the lowest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM2.5 value in August 2018?,Tirupati 1326,1676,spatial_aggregation,Which city has the highest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Name the city with the highest median PM10 value in November 2018.,Greater Noida 1327,1677,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the third-highest 25th percentile for PM2.5 during November 2024.,Haryana 1328,1679,spatial_aggregation,Which state has the highest median PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the peak median PM2.5 concentration for September 2023.,Jharkhand 1329,1680,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Find the station with the second-lowest 75th percentile for PM2.5 in November 2020.,"Udyogamandal, Eloor - Kerala PCB" 1330,1681,spatial_aggregation,Which state has the 2nd highest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state was second in terms of highest average PM2.5 for March 2022?,Delhi 1331,1682,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Name the station showing the second-highest 25th percentile of PM2.5 for March 2020.,"Railway Colony, Guwahati - PCBA" 1332,1683,spatial_aggregation,Which state has the 2nd highest average PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that was second in terms of highest average PM2.5 for April 2022.,Haryana 1333,1684,spatial_aggregation,Which city has the 2nd highest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city was second in terms of highest average PM2.5 for November 2024?,Ghaziabad 1334,1687,spatial_aggregation,Which state has the highest 25th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state exhibited the highest 25th percentile for PM10 during August 2023?,Himachal Pradesh 1335,1688,spatial_aggregation,Which city has the 2nd highest average PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Name the city that was second in terms of highest average PM2.5 for June 2018.,Jodhpur 1336,1689,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 25th percentile for PM10 in April 2018.,Nagpur 1337,1690,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the second-lowest mean PM2.5 reading for January 2020?,Satna 1338,1692,spatial_aggregation,Which city has the 3rd highest average PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Find the city that had the third-highest average PM10 in November 2018.,Noida 1339,1693,spatial_aggregation,Which station has the 2nd highest median PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the second-highest median PM2.5 in July 2023?,"GIDC, Nandesari - Nandesari Ind. Association" 1340,1694,spatial_aggregation,Which state has the 3rd highest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Name the state with the third-highest mean PM2.5 concentration in June 2021.,Rajasthan 1341,1696,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest 25th percentile PM2.5 value in June 2018?,Satna 1342,1698,spatial_aggregation,Which station has the 2nd highest median PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Find the station with the second-highest median PM2.5 in September 2023.,"Sector-51, Gurugram - HSPCB" 1343,1699,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the third-lowest median PM2.5 concentration in December 2021?,Arunachal Pradesh 1344,1700,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Name the station with the third-lowest 25th percentile for PM10 in September 2018.,"Hebbal, Bengaluru - KSPCB" 1345,1702,spatial_aggregation,Which state has the highest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile PM10 reading for June 2020.,Odisha 1346,1703,spatial_aggregation,Which station has the lowest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest median PM10 during June 2024?,"Diwator Nagar, Koppal - KSPCB" 1347,1704,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest 25th percentile of PM10 in October 2020.,Aizawl 1348,1706,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM2.5 in July 2018.,Maharashtra 1349,1707,spatial_aggregation,Which city has the 2nd lowest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM10 for October 2024.,Gangtok 1350,1708,spatial_aggregation,Which state has the highest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest median PM10 in May 2021?,Uttar Pradesh 1351,1709,spatial_aggregation,Which station has the highest 75th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 75th percentile of PM10 in December 2024.,"Muradpur, Patna - BSPCB" 1352,1710,spatial_aggregation,Which station has the 2nd highest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM2.5 in December 2021?,"Nehru Nagar, Delhi - DPCC" 1353,1712,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest median PM2.5 value in December 2018.,"PWD Grounds, Vijayawada - APPCB" 1354,1713,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM10 during December 2023?,Mizoram 1355,1714,spatial_aggregation,Which state has the 2nd highest average PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest average PM2.5 in September 2021.,Gujarat 1356,1715,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 25th percentile of PM2.5 in January 2021?,Delhi 1357,1716,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 75th percentile of PM2.5 in November 2022.,Bettiah 1358,1718,spatial_aggregation,Which city has the lowest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest median PM10 in May 2024?,Chengalpattu 1359,1719,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest median PM2.5 in October 2018.,"BTM Layout, Bengaluru - CPCB" 1360,1720,spatial_aggregation,Which station has the highest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest average PM2.5 in September 2019?,"Lumpyngngad, Shillong - Meghalaya PCB" 1361,1721,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 25th percentile of PM2.5 in March 2020.,Guwahati 1362,1722,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest 75th percentile of PM2.5 value in April 2022.,Muzaffarnagar 1363,1723,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest 25th percentile of PM2.5 during February 2023?,Byrnihat 1364,1724,spatial_aggregation,Which station has the highest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 25th percentile of PM10 in June 2019.,"Dwarka-Sector 8, Delhi - DPCC" 1365,1725,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 75th percentile of PM10 in January 2019?,Delhi 1366,1726,spatial_aggregation,Which state has the lowest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest average PM2.5 in May 2023.,Sikkim 1367,1728,spatial_aggregation,Which city has the 2nd highest median PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest median PM10 in January 2021?,Lucknow 1368,1729,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 75th percentile of PM2.5 in June 2022.,Mizoram 1369,1731,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM2.5 in November 2021.,Koppal 1370,1732,spatial_aggregation,Which city has the 2nd lowest average PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest average PM10 value in June 2020.,Pune 1371,1735,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in June 2021?,"ITO, Delhi - CPCB" 1372,1736,spatial_aggregation,Which state has the 3rd lowest median PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest median PM10 in October 2023.,Arunachal Pradesh 1373,1737,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 for January 2020.,Satna 1374,1739,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 25th percentile of PM2.5 in August 2022.,Himachal Pradesh 1375,1740,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM10 in February 2024?,Sikkim 1376,1741,spatial_aggregation,Which station has the highest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM2.5 in August 2019.,"Deshpande Nagar, Hubballi - KSPCB" 1377,1742,spatial_aggregation,Which state has the lowest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest average PM10 value in February 2018.,Kerala 1378,1743,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest 75th percentile of PM2.5 during April 2018?,Gaya 1379,1745,spatial_aggregation,Which station has the lowest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM10 in January 2024?,"GIDC, Nandesari - Nandesari Ind. Association" 1380,1746,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest 75th percentile of PM10 in September 2022.,"Vasai West, Mumbai - MPCB" 1381,1747,spatial_aggregation,Which station has the lowest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM2.5 for March 2020.,"Udyogamandal, Eloor - Kerala PCB" 1382,1748,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM2.5 in August 2024?,Sikkim 1383,1749,spatial_aggregation,Which station has the 2nd highest average PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the 2nd highest average PM2.5 in April 2023.,"Muradpur, Patna - BSPCB" 1384,1750,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM2.5 in November 2018?,"Chikkaballapur Rural, Chikkaballapur - KSPCB" 1385,1751,spatial_aggregation,Which city has the 3rd highest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM2.5 in June 2023.,Byrnihat 1386,1752,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 75th percentile of PM2.5 value in June 2022.,"Talkatora District Industries Center, Lucknow - CPCB" 1387,1754,spatial_aggregation,Which state has the lowest median PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest median PM2.5 in March 2024.,Jammu and Kashmir 1388,1756,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest 25th percentile of PM2.5 in May 2020.,Mizoram 1389,1757,spatial_aggregation,Which city has the 3rd highest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM10 for June 2018.,Ghaziabad 1390,1758,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in August 2021?,Jodhpur 1391,1759,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 75th percentile of PM2.5 in June 2018.,"Alandur Bus Depot, Chennai - CPCB" 1392,1760,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 25th percentile of PM2.5 in December 2024?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1393,1762,spatial_aggregation,Which station has the 2nd highest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest median PM2.5 value in February 2022.,"Town Hall, Munger - BSPCB" 1394,1763,spatial_aggregation,Which station has the highest 75th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest 75th percentile of PM10 during May 2024?,"Shadipur, Delhi - CPCB" 1395,1764,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 25th percentile of PM10 in July 2018.,"Gangapur Road, Nashik - MPCB" 1396,1765,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 25th percentile of PM2.5 in December 2022?,Darbhanga 1397,1766,spatial_aggregation,Which city has the 2nd highest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest median PM2.5 in January 2018.,Kanpur 1398,1769,spatial_aggregation,Which city has the lowest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest median PM10 in August 2021.,Udupi 1399,1770,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 75th percentile of PM10 in April 2018?,Karnataka 1400,1771,spatial_aggregation,Which state has the lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in June 2018.,Kerala 1401,1772,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest 75th percentile of PM2.5 value in December 2024.,Karnataka 1402,1773,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest 75th percentile of PM10 during March 2018?,Ghaziabad 1403,1774,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM2.5 in February 2020.,Assam 1404,1775,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM10 in March 2020?,"Dwarka-Sector 8, Delhi - DPCC" 1405,1776,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 25th percentile of PM2.5 in February 2023.,Bihar 1406,1778,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM2.5 in January 2022?,Puducherry 1407,1779,spatial_aggregation,Which city has the highest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM2.5 in March 2022.,Katihar 1408,1780,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM10 in February 2021?,Kolar 1409,1781,spatial_aggregation,Which station has the 3rd lowest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest median PM10 in July 2022.,"Stuart Hill, Madikeri - KSPCB" 1410,1783,spatial_aggregation,Which city has the lowest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest median PM2.5 during September 2020?,Aizawl 1411,1785,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM2.5 in September 2024?,"Kumaran College, Tirupur - TNPCB" 1412,1787,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM2.5 for November 2022.,Delhi 1413,1788,spatial_aggregation,Which station has the highest 75th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 75th percentile of PM10 in July 2022?,"D M Colony, Bihar Sharif - BSPCB" 1414,1790,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 75th percentile of PM10 in August 2021?,Haryana 1415,1791,spatial_aggregation,Which city has the 2nd lowest median PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest median PM10 in July 2019.,Chamarajanagar 1416,1792,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 25th percentile of PM2.5 value in May 2019.,"Nathu Colony, Ballabgarh - HSPCB" 1417,1793,spatial_aggregation,Which city has the highest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM10 during April 2019?,Ghaziabad 1418,1795,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM2.5 in September 2023?,"Central Academy for SFS, Byrnihat - PCBA" 1419,1797,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 75th percentile of PM2.5 for March 2021.,"Bawana, Delhi - DPCC" 1420,1800,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM2.5 in November 2018?,"Anand Vihar, Delhi - DPCC" 1421,1801,spatial_aggregation,Which station has the highest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM2.5 in May 2019.,"North Campus, DU, Delhi - IMD" 1422,1803,spatial_aggregation,Which state has the 2nd highest average PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM2.5 during February 2018?,Delhi 1423,1804,spatial_aggregation,Which city has the 3rd lowest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest average PM10 in May 2018.,Vijayawada 1424,1806,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest 75th percentile of PM2.5 in April 2019.,Ballabgarh 1425,1807,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM2.5 for May 2018.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 1426,1808,spatial_aggregation,Which state has the 3rd lowest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM10 in February 2020?,Meghalaya 1427,1809,spatial_aggregation,Which station has the 3rd lowest median PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest median PM10 in June 2020.,"Karve Road, Pune - MPCB" 1428,1810,spatial_aggregation,Which state has the 2nd highest median PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM10 in August 2020?,Uttar Pradesh 1429,1811,spatial_aggregation,Which city has the 3rd lowest median PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM10 in August 2022.,Maihar 1430,1812,spatial_aggregation,Which city has the 2nd highest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest average PM2.5 value in November 2018.,Muzaffarpur 1431,1813,spatial_aggregation,Which city has the highest median PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest median PM10 during January 2019?,Bahadurgarh 1432,1814,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 25th percentile of PM2.5 in December 2022.,"DRCC Anandpur, Begusarai - BSPCB" 1433,1815,spatial_aggregation,Which city has the lowest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM2.5 in December 2018?,Satna 1434,1816,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM2.5 in May 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1435,1817,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM2.5 for October 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1436,1818,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in July 2024?,Sri Ganganagar 1437,1819,spatial_aggregation,Which state has the 2nd highest median PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM10 in October 2022.,Haryana 1438,1820,spatial_aggregation,Which station has the highest 25th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM10 in November 2020?,"Mundka, Delhi - DPCC" 1439,1822,spatial_aggregation,Which state has the 2nd highest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest median PM2.5 value in March 2023.,Bihar 1440,1823,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM2.5 during June 2023?,Sikkim 1441,1827,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 for April 2021.,"Udyogamandal, Eloor - Kerala PCB" 1442,1828,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 25th percentile of PM2.5 in July 2019?,"Hardev Nagar, Bathinda - PPCB" 1443,1829,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM2.5 in January 2023.,Jammu and Kashmir 1444,1830,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM2.5 in June 2024?,Koppal 1445,1831,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM10 in June 2024.,Greater Noida 1446,1832,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 75th percentile of PM2.5 value in July 2022.,"Civic Center, Bhilai - Bhilai Steel Plant" 1447,1834,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 25th percentile of PM10 in May 2018.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1448,1835,spatial_aggregation,Which city has the 2nd highest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in May 2019?,Ghaziabad 1449,1836,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM2.5 in January 2018.,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 1450,1837,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM2.5 for October 2020.,"Bawana, Delhi - DPCC" 1451,1838,spatial_aggregation,Which station has the highest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest median PM10 in June 2022?,"Loni, Ghaziabad - UPPCB" 1452,1839,spatial_aggregation,Which city has the 3rd highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest median PM10 in February 2023.,Begusarai 1453,1840,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 75th percentile of PM2.5 in November 2018?,Karnataka 1454,1841,spatial_aggregation,Which state has the highest 25th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 25th percentile of PM10 in April 2021.,Uttar Pradesh 1455,1843,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd lowest 25th percentile of PM2.5 during October 2019?,Eloor 1456,1845,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM10 in January 2024?,"GIDC, Nandesari - Nandesari Ind. Association" 1457,1846,spatial_aggregation,Which station has the highest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest median PM10 in February 2021.,"Chandni Chowk, Delhi - IITM" 1458,1847,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 75th percentile of PM2.5 for September 2020.,Haryana 1459,1848,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 25th percentile of PM2.5 in December 2024?,Mizoram 1460,1849,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest 25th percentile of PM10 in July 2019.,"Collectorate, Jodhpur - RSPCB" 1461,1850,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM10 in July 2019?,"Pimpleshwar Mandir, Thane - MPCB" 1462,1851,spatial_aggregation,Which state has the 3rd lowest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM10 in October 2021.,Puducherry 1463,1852,spatial_aggregation,Which city has the 2nd lowest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest average PM10 value in February 2023.,Nandesari 1464,1854,spatial_aggregation,Which station has the highest 25th percentile of PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 25th percentile of PM10 in November 2018.,"Wazirpur, Delhi - DPCC" 1465,1856,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 25th percentile of PM2.5 in September 2020.,Kozhikode 1466,1858,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest 75th percentile of PM10 in February 2019?,"Wazirpur, Delhi - DPCC" 1467,1859,spatial_aggregation,Which state has the 2nd lowest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest average PM10 in May 2024.,Sikkim 1468,1860,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM2.5 in February 2024?,Tripura 1469,1861,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in October 2021.,Ballabgarh 1470,1862,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 75th percentile of PM10 value in September 2023.,Udupi 1471,1863,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest 75th percentile of PM2.5 during June 2024?,Punjab 1472,1864,spatial_aggregation,Which station has the 2nd highest median PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest median PM10 in December 2018.,"Anand Vihar, Delhi - DPCC" 1473,1865,spatial_aggregation,Which state has the 2nd lowest median PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest median PM10 in December 2022?,Mizoram 1474,1866,spatial_aggregation,Which state has the 3rd lowest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM10 in August 2022.,Meghalaya 1475,1867,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM2.5 for May 2023.,Aizawl 1476,1869,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 75th percentile of PM2.5 in January 2024.,Bhagalpur 1477,1871,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in April 2023.,Jharkhand 1478,1872,spatial_aggregation,Which city has the 3rd lowest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest median PM10 value in February 2020.,Mysuru 1479,1873,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 75th percentile of PM10 during April 2019?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 1480,1874,spatial_aggregation,Which state has the 2nd highest average PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest average PM10 in December 2019.,Uttar Pradesh 1481,1875,spatial_aggregation,Which station has the 2nd highest average PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM2.5 in September 2018?,"Sector - 125, Noida - UPPCB" 1482,1878,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM10 in November 2022?,Sikkim 1483,1879,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 75th percentile of PM10 in April 2023.,Sikkim 1484,1880,spatial_aggregation,Which state has the 2nd lowest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest average PM10 in October 2021?,Mizoram 1485,1882,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest 75th percentile of PM2.5 value in June 2019.,"Shadipur, Delhi - CPCB" 1486,1884,spatial_aggregation,Which city has the lowest average PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest average PM10 in August 2020.,Shillong 1487,1885,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 25th percentile of PM10 in November 2023?,Bhiwadi 1488,1886,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest 75th percentile of PM10 in May 2020.,Coimbatore 1489,1887,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM2.5 for April 2018.,Delhi 1490,1888,spatial_aggregation,Which state has the highest 25th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile of PM10 in July 2019?,Delhi 1491,1889,spatial_aggregation,Which city has the 2nd highest median PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest median PM10 in December 2022.,Begusarai 1492,1890,spatial_aggregation,Which city has the 2nd highest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in April 2019?,Singrauli 1493,1892,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest 75th percentile of PM2.5 value in June 2020.,Ratlam 1494,1894,spatial_aggregation,Which state has the highest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest median PM10 in September 2021.,Jammu and Kashmir 1495,1895,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM2.5 in February 2024?,Araria 1496,1896,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 75th percentile of PM2.5 in February 2020.,Delhi 1497,1897,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 25th percentile of PM10 for May 2023.,Chhapra 1498,1898,spatial_aggregation,Which city has the 3rd highest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest average PM10 in April 2024?,Patna 1499,1899,spatial_aggregation,Which city has the highest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest average PM2.5 in March 2022.,Katihar 1500,1900,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in March 2019?,Rajamahendravaram 1501,1901,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 25th percentile of PM10 in June 2018.,Hyderabad 1502,1902,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 25th percentile of PM2.5 value in March 2023.,Mizoram 1503,1903,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest median PM2.5 during June 2023?,Aizawl 1504,1904,spatial_aggregation,Which state has the 3rd highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM2.5 in March 2020.,Bihar 1505,1906,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest 25th percentile of PM2.5 in August 2024.,Panipat 1506,1907,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 25th percentile of PM10 for October 2023.,"Knowledge Park - V, Greater Noida - UPPCB" 1507,1908,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile of PM10 in August 2024?,Byrnihat 1508,1909,spatial_aggregation,Which station has the 2nd lowest average PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM10 in November 2018.,"Chikkaballapur Rural, Chikkaballapur - KSPCB" 1509,1910,spatial_aggregation,Which city has the lowest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest average PM2.5 in October 2020?,Aizawl 1510,1911,spatial_aggregation,Which state has the highest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 25th percentile of PM10 in January 2020.,Assam 1511,1912,spatial_aggregation,Which state has the 3rd lowest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest average PM10 value in April 2022.,Jammu and Kashmir 1512,1913,spatial_aggregation,Which city has the 2nd highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest median PM2.5 during December 2021?,Chhapra 1513,1914,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 25th percentile of PM10 in July 2018.,Delhi 1514,1915,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 25th percentile of PM10 in January 2018?,Kerala 1515,1916,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest 75th percentile of PM2.5 in May 2020.,"Punjabi Bagh, Delhi - DPCC" 1516,1917,spatial_aggregation,Which state has the lowest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM10 for November 2021.,Meghalaya 1517,1919,spatial_aggregation,Which state has the 2nd lowest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM10 in June 2019.,Tamil Nadu 1518,1922,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 25th percentile of PM2.5 value in March 2020.,Punjab 1519,1923,spatial_aggregation,Which station has the highest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest median PM10 during June 2019?,"Dwarka-Sector 8, Delhi - DPCC" 1520,1925,spatial_aggregation,Which station has the highest 75th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM10 in April 2023?,"Kanuru, Vijayawada - APPCB" 1521,1926,spatial_aggregation,Which city has the highest median PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest median PM2.5 in December 2019.,Ghaziabad 1522,1927,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 75th percentile of PM10 for July 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1523,1928,spatial_aggregation,Which station has the 3rd highest average PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average PM10 in January 2019?,"Jahangirpuri, Delhi - DPCC" 1524,1929,spatial_aggregation,Which city has the highest 25th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest 25th percentile of PM10 in October 2022.,Dharuhera 1525,1930,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 75th percentile of PM2.5 in September 2020?,Lucknow 1526,1932,spatial_aggregation,Which station has the 3rd highest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest median PM10 value in November 2021.,"Wazirpur, Delhi - DPCC" 1527,1935,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM10 in March 2021?,"Mundka, Delhi - DPCC" 1528,1936,spatial_aggregation,Which station has the 2nd lowest average PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station that had the 2nd lowest average PM10 in July 2021.,"Brahmagiri, Udupi - KSPCB" 1529,1937,spatial_aggregation,Which city has the 3rd highest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM2.5 for May 2019.,Bhiwadi 1530,1938,spatial_aggregation,Which city has the 3rd highest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest average PM10 in August 2024?,Byrnihat 1531,1939,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest median PM2.5 in November 2019.,"PWD Grounds, Vijayawada - APPCB" 1532,1940,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM2.5 in May 2019?,"Bandra, Mumbai - MPCB" 1533,1941,spatial_aggregation,Which state has the lowest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM10 in January 2023.,Jammu and Kashmir 1534,1943,spatial_aggregation,Which state has the 2nd highest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest median PM10 during February 2022?,Bihar 1535,1944,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM10 in October 2023.,"Sector - 62, Noida - IMD" 1536,1945,spatial_aggregation,Which station has the highest average PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest average PM2.5 in December 2018?,"Nehru Nagar, Delhi - DPCC" 1537,1947,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest average PM2.5 for January 2023.,Arunachal Pradesh 1538,1948,spatial_aggregation,Which station has the lowest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest median PM2.5 in January 2019?,"Bandhavgar Colony, Satna - Birla Cement" 1539,1949,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 75th percentile of PM10 in February 2024.,Puducherry 1540,1950,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM2.5 in December 2018?,Uttar Pradesh 1541,1952,spatial_aggregation,Which station has the 3rd lowest median PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest median PM10 value in October 2018.,"PWD Grounds, Vijayawada - APPCB" 1542,1953,spatial_aggregation,Which state has the highest median PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest median PM10 during May 2020?,Uttar Pradesh 1543,1954,spatial_aggregation,Which city has the highest average PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest average PM2.5 in April 2021.,Singrauli 1544,1955,spatial_aggregation,Which state has the 2nd highest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM10 in August 2021?,Haryana 1545,1957,spatial_aggregation,Which station has the highest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM2.5 for August 2024.,"Sardar Patel Nagar, Dhanbad - JSPCB" 1546,1958,spatial_aggregation,Which station has the highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest average PM2.5 in March 2020?,"Mini Secretariat, Charkhi Dadri - HSPCB" 1547,1960,spatial_aggregation,Which state has the 3rd highest average PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest average PM2.5 in January 2024?,Chandigarh 1548,1961,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest 25th percentile of PM10 in November 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1549,1962,spatial_aggregation,Which station has the highest median PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest median PM10 value in January 2019.,"Wazirpur, Delhi - DPCC" 1550,1966,spatial_aggregation,Which station has the lowest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest median PM10 in October 2024.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 1551,1967,spatial_aggregation,Which city has the 3rd highest average PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM2.5 for June 2022.,Fatehabad 1552,1968,spatial_aggregation,Which city has the 2nd highest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest median PM2.5 in July 2021?,Rohtak 1553,1969,spatial_aggregation,Which city has the highest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM2.5 in June 2022.,Bhiwadi 1554,1970,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM2.5 in February 2023?,"Sahilara, Maihar - KJS Cements" 1555,1971,spatial_aggregation,Which state has the 3rd highest average PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest average PM10 in January 2021.,West Bengal 1556,1972,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 75th percentile of PM2.5 value in November 2022.,Bihar 1557,1973,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest 25th percentile of PM2.5 during January 2020?,Kerala 1558,1974,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 25th percentile of PM2.5 in July 2024.,Nandesari 1559,1975,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 25th percentile of PM2.5 in December 2021?,Chhapra 1560,1977,spatial_aggregation,Which state has the 2nd highest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest average PM2.5 for October 2020.,Uttar Pradesh 1561,1978,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM10 in May 2023?,Jharkhand 1562,1979,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 25th percentile of PM2.5 in February 2021.,Ghaziabad 1563,1980,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in March 2018?,Bhiwadi 1564,1981,spatial_aggregation,Which city has the highest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 25th percentile of PM10 in January 2021.,Lucknow 1565,1982,spatial_aggregation,Which city has the 3rd highest median PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest median PM10 value in October 2022.,Ghaziabad 1566,1983,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM10 during May 2024?,Sikkim 1567,1984,spatial_aggregation,Which station has the highest median PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest median PM2.5 in June 2021.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 1568,1985,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 25th percentile of PM2.5 in January 2021?,Assam 1569,1986,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 75th percentile of PM2.5 in November 2023.,Delhi 1570,1988,spatial_aggregation,Which station has the 2nd highest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest average PM10 in July 2022?,"D M Colony, Bihar Sharif - BSPCB" 1571,1989,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 25th percentile of PM2.5 in March 2018.,Karnataka 1572,1990,spatial_aggregation,Which city has the 2nd highest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM2.5 in April 2019?,Thane 1573,1991,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 75th percentile of PM10 in November 2024.,"Wazirpur, Delhi - DPCC" 1574,1992,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest 25th percentile of PM2.5 value in August 2021.,Rajasthan 1575,1993,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest 25th percentile of PM2.5 during February 2018?,Tamil Nadu 1576,1995,spatial_aggregation,Which state has the highest 75th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 75th percentile of PM10 in December 2019?,Delhi 1577,1996,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM10 in July 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1578,1997,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 for October 2024.,Aizawl 1579,1999,spatial_aggregation,Which state has the lowest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM2.5 in March 2020.,Chandigarh 1580,2001,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM10 in December 2020.,Aizawl 1581,2004,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 75th percentile of PM10 in November 2023.,Puducherry 1582,2006,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 25th percentile of PM10 in July 2024.,Imphal 1583,2008,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile of PM10 in November 2018?,"Tirumala, Tirupati - APPCB" 1584,2009,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest 75th percentile of PM2.5 in October 2021.,Shillong 1585,2013,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 75th percentile of PM10 during July 2021?,"Panchal Nagar, Gadag - KSPCB" 1586,2014,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest median PM2.5 in November 2024.,Manipur 1587,2015,spatial_aggregation,Which station has the highest median PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM2.5 in May 2024?,"Shadipur, Delhi - CPCB" 1588,2016,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 25th percentile of PM10 in February 2024.,Sikkim 1589,2017,spatial_aggregation,Which city has the highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM10 for July 2022.,Bihar Sharif 1590,2018,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 75th percentile of PM2.5 in August 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1591,2019,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest 75th percentile of PM2.5 in October 2024.,Haryana 1592,2020,spatial_aggregation,Which station has the highest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM10 in February 2024?,"Central Academy for SFS, Byrnihat - PCBA" 1593,2021,spatial_aggregation,Which state has the highest 25th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 25th percentile of PM10 in March 2019.,Uttar Pradesh 1594,2022,spatial_aggregation,Which state has the 3rd highest median PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest median PM10 value in February 2019.,Uttar Pradesh 1595,2023,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest 25th percentile of PM2.5 during September 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 1596,2024,spatial_aggregation,Which state has the highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest median PM2.5 in December 2021.,Delhi 1597,2025,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 75th percentile of PM2.5 in June 2020?,Maharashtra 1598,2026,spatial_aggregation,Which station has the lowest average PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest average PM10 in October 2022.,"Zero Point GICI, Gangtok - SSPCB" 1599,2027,spatial_aggregation,Which city has the 2nd lowest median PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM10 for June 2023.,Gangtok 1600,2029,spatial_aggregation,Which station has the 3rd highest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest median PM2.5 in November 2024.,"Vivek Vihar, Delhi - DPCC" 1601,2030,spatial_aggregation,Which station has the highest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM10 in September 2022?,"Anand Vihar, Delhi - DPCC" 1602,2032,spatial_aggregation,Which city has the highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest average PM2.5 value in March 2020.,Charkhi Dadri 1603,2034,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in November 2020.,Haryana 1604,2035,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM2.5 in December 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1605,2036,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 25th percentile of PM2.5 in February 2018.,"Bandhavgar Colony, Satna - Birla Cement" 1606,2037,spatial_aggregation,Which station has the highest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM10 for September 2021.,"Kurla, Mumbai - MPCB" 1607,2039,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 25th percentile of PM2.5 in December 2023.,Hanumangarh 1608,2040,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM10 in February 2022?,Udupi 1609,2043,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest 75th percentile of PM10 during June 2024?,Chandigarh 1610,2044,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest average PM2.5 in December 2022.,Chhattisgarh 1611,2045,spatial_aggregation,Which station has the lowest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in June 2024?,"Kumaran College, Tirupur - TNPCB" 1612,2046,spatial_aggregation,Which state has the 3rd lowest median PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest median PM10 in January 2024.,Manipur 1613,2047,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for December 2024.,"PCBL Residential Complex, Durgapur - WBPCB" 1614,2048,spatial_aggregation,Which city has the 2nd highest average PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM10 in June 2023?,Begusarai 1615,2049,spatial_aggregation,Which city has the highest average PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest average PM10 in September 2022.,Tirupur 1616,2050,spatial_aggregation,Which city has the 2nd highest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in September 2024?,Sri Ganganagar 1617,2051,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 25th percentile of PM2.5 in July 2018.,Muzaffarnagar 1618,2052,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 75th percentile of PM2.5 value in October 2022.,Aizawl 1619,2053,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM2.5 during January 2019?,Uttar Pradesh 1620,2054,spatial_aggregation,Which station has the highest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM10 in November 2020.,"Bawana, Delhi - DPCC" 1621,2055,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM2.5 in March 2024?,Delhi 1622,2057,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM10 for February 2019.,"Mundka, Delhi - DPCC" 1623,2058,spatial_aggregation,Which station has the highest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest median PM10 in April 2019?,"Mundka, Delhi - DPCC" 1624,2060,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM10 in November 2022?,Meghalaya 1625,2061,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest 25th percentile of PM10 in March 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 1626,2062,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM2.5 value in January 2022.,Kishanganj 1627,2063,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during July 2024?,"Knowledge Park - V, Greater Noida - UPPCB" 1628,2064,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest average PM2.5 in August 2018.,Telangana 1629,2065,spatial_aggregation,Which station has the 3rd highest median PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in April 2022?,"Talkatora District Industries Center, Lucknow - CPCB" 1630,2066,spatial_aggregation,Which station has the lowest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest average PM10 in October 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1631,2067,spatial_aggregation,Which state has the 2nd lowest average PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM10 for March 2020.,Tamil Nadu 1632,2068,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM2.5 in May 2020?,"Udyogamandal, Eloor - Kerala PCB" 1633,2069,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest 25th percentile of PM10 in November 2024.,"Mundka, Delhi - DPCC" 1634,2070,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest average PM2.5 in May 2022?,Sikkim 1635,2071,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest median PM2.5 in January 2021.,Puducherry 1636,2072,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM10 value in July 2018.,Jodhpur 1637,2073,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 75th percentile of PM2.5 during October 2023?,"Bawana, Delhi - DPCC" 1638,2074,spatial_aggregation,Which station has the 3rd lowest average PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest average PM10 in May 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1639,2075,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM2.5 in February 2022?,"GIDC, Nandesari - Nandesari Ind. Association" 1640,2076,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 75th percentile of PM2.5 in September 2024.,Delhi 1641,2078,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 25th percentile of PM2.5 in February 2023?,"Sector-19A Nerul, Navi Mumbai - IITM" 1642,2080,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in September 2019?,"Manali, Chennai - CPCB" 1643,2082,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM10 value in October 2019.,Greater Noida 1644,2083,spatial_aggregation,Which state has the 2nd lowest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest average PM10 during February 2020?,Meghalaya 1645,2084,spatial_aggregation,Which city has the 3rd highest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest median PM10 in September 2018.,Bhiwadi 1646,2085,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM2.5 in November 2023?,Puducherry 1647,2086,spatial_aggregation,Which city has the 3rd highest average PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest average PM2.5 in November 2023.,Greater Noida 1648,2087,spatial_aggregation,Which state has the 3rd highest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest median PM2.5 for April 2019.,Haryana 1649,2088,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM2.5 in August 2019?,Karnataka 1650,2090,spatial_aggregation,Which city has the highest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM10 in February 2019?,Talcher 1651,2091,spatial_aggregation,Which station has the highest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 75th percentile of PM10 in January 2023.,"Samanpura, Patna - BSPCB" 1652,2092,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest median PM2.5 value in July 2021.,"Diwator Nagar, Koppal - KSPCB" 1653,2094,spatial_aggregation,Which station has the highest median PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest median PM2.5 in July 2024.,"MIT-Daudpur Kothi, Muzaffarpur - BSPCB" 1654,2095,spatial_aggregation,Which city has the highest 25th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 25th percentile of PM10 in June 2022?,Sonipat 1655,2097,spatial_aggregation,Which state has the lowest median PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM2.5 for February 2024.,Jammu and Kashmir 1656,2098,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 75th percentile of PM10 in April 2024?,Hajipur 1657,2099,spatial_aggregation,Which state has the 3rd lowest median PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest median PM10 in December 2021.,Karnataka 1658,2100,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 25th percentile of PM2.5 in March 2019?,Andhra Pradesh 1659,2101,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM2.5 in April 2019.,Amaravati 1660,2102,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest average PM2.5 value in March 2020.,Chandrapur 1661,2103,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 75th percentile of PM2.5 during February 2022?,"Anand Vihar, Delhi - DPCC" 1662,2104,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 25th percentile of PM10 in October 2019.,Kerala 1663,2105,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in February 2020?,Mysuru 1664,2106,spatial_aggregation,Which city has the 3rd lowest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest median PM10 in September 2022.,Bhilai 1665,2108,spatial_aggregation,Which city has the 2nd highest average PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM2.5 in October 2022?,Begusarai 1666,2109,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 25th percentile of PM2.5 in February 2021.,"Bandra, Mumbai - MPCB" 1667,2110,spatial_aggregation,Which city has the 2nd highest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM10 in February 2024?,Sri Ganganagar 1668,2112,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 25th percentile of PM10 value in March 2019.,"Nathu Colony, Ballabgarh - HSPCB" 1669,2113,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest average PM2.5 during December 2024?,Kerala 1670,2114,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest 25th percentile of PM2.5 in February 2023.,Ramanathapuram 1671,2116,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest 75th percentile of PM2.5 in March 2020.,"Govt. High School Shikarpur, Patna - BSPCB" 1672,2117,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM2.5 for March 2022.,Jammu and Kashmir 1673,2118,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM2.5 in July 2019?,Andhra Pradesh 1674,2119,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 75th percentile of PM2.5 in August 2018.,"Talkatora District Industries Center, Lucknow - CPCB" 1675,2120,spatial_aggregation,Which state has the lowest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest median PM10 in September 2018?,Odisha 1676,2121,spatial_aggregation,Which city has the 2nd highest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM10 in August 2024.,Sri Ganganagar 1677,2122,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 25th percentile of PM10 value in July 2018.,Telangana 1678,2123,spatial_aggregation,Which station has the lowest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest median PM10 during October 2020?,"Stuart Hill, Madikeri - KSPCB" 1679,2124,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in March 2018.,Gujarat 1680,2125,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM10 in April 2024?,Assam 1681,2128,spatial_aggregation,Which station has the highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest median PM2.5 in January 2022?,"Anand Vihar, Delhi - DPCC" 1682,2129,spatial_aggregation,Which station has the 3rd highest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest average PM2.5 in December 2020.,"Talkatora District Industries Center, Lucknow - CPCB" 1683,2130,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in August 2019?,"RIICO Ind. Area III, Bhiwadi - RSPCB" 1684,2132,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest 25th percentile of PM10 value in February 2024.,Araria 1685,2133,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 75th percentile of PM2.5 during February 2023?,Mizoram 1686,2134,spatial_aggregation,Which state has the lowest average PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM2.5 in December 2018.,Karnataka 1687,2135,spatial_aggregation,Which state has the highest average PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM2.5 in September 2024?,Delhi 1688,2136,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest 75th percentile of PM2.5 in June 2022.,Bhiwadi 1689,2138,spatial_aggregation,Which city has the 3rd highest median PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest median PM2.5 in January 2021?,Noida 1690,2139,spatial_aggregation,Which state has the lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM2.5 in May 2018.,Kerala 1691,2140,spatial_aggregation,Which station has the 2nd highest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest median PM10 in May 2024?,"Knowledge Park - V, Greater Noida - UPPCB" 1692,2141,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in March 2024.,Byrnihat 1693,2142,spatial_aggregation,Which state has the lowest average PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest average PM10 value in January 2021.,Meghalaya 1694,2143,spatial_aggregation,Which state has the highest 25th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest 25th percentile of PM10 during September 2018?,Haryana 1695,2145,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM2.5 in November 2019?,Noida 1696,2146,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest average PM2.5 in February 2021.,Rupnagar 1697,2147,spatial_aggregation,Which station has the 2nd highest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest median PM10 for November 2018.,"Wazirpur, Delhi - DPCC" 1698,2148,spatial_aggregation,Which station has the 3rd highest average PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average PM2.5 in August 2020?,"Nathu Colony, Ballabgarh - HSPCB" 1699,2150,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 25th percentile of PM2.5 in September 2022?,Rajasthan 1700,2151,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest 25th percentile of PM2.5 in December 2021.,"Jahangirpuri, Delhi - DPCC" 1701,2152,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest 75th percentile of PM2.5 value in March 2018.,"Secretariat, Amaravati - APPCB" 1702,2153,spatial_aggregation,Which station has the 2nd highest median PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest median PM2.5 during September 2024?,"GIDC, Nandesari - Nandesari Ind. Association" 1703,2154,spatial_aggregation,Which state has the 3rd highest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM2.5 in July 2018.,Tamil Nadu 1704,2155,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM2.5 in January 2018?,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 1705,2156,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 25th percentile of PM2.5 in September 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1706,2157,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 25th percentile of PM2.5 for May 2021.,Jodhpur 1707,2158,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 25th percentile of PM2.5 in October 2023?,Silchar 1708,2159,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest median PM2.5 in March 2023.,"SVPI Airport Hansol, Ahmedabad - IITM" 1709,2160,spatial_aggregation,Which city has the 3rd highest average PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in May 2022?,Noida 1710,2161,spatial_aggregation,Which station has the 3rd highest median PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest median PM10 in April 2021.,"Mundka, Delhi - DPCC" 1711,2162,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 25th percentile of PM2.5 value in July 2023.,Tripura 1712,2163,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM2.5 during July 2022?,Chhattisgarh 1713,2165,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 75th percentile of PM2.5 in December 2018?,"PWD Grounds, Vijayawada - APPCB" 1714,2166,spatial_aggregation,Which station has the highest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest median PM2.5 in March 2020.,"Railway Colony, Guwahati - PCBA" 1715,2167,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile of PM2.5 for July 2023.,Mizoram 1716,2168,spatial_aggregation,Which state has the highest average PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM10 in October 2019?,Uttar Pradesh 1717,2170,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 25th percentile of PM10 in December 2024?,Meghalaya 1718,2171,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM2.5 in December 2018.,Karnataka 1719,2172,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 75th percentile of PM2.5 value in April 2023.,Sikkim 1720,2173,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM10 during April 2020?,Mizoram 1721,2174,spatial_aggregation,Which city has the 3rd highest median PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest median PM2.5 in February 2018.,Patna 1722,2175,spatial_aggregation,Which state has the highest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM2.5 in July 2021?,Delhi 1723,2176,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest 25th percentile of PM10 in June 2020.,Mizoram 1724,2178,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM10 in June 2021?,Gadag 1725,2179,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM2.5 in December 2020.,"Bandra, Mumbai - MPCB" 1726,2180,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM2.5 in November 2024?,"Kodungaiyur, Chennai - TNPCB" 1727,2181,spatial_aggregation,Which station has the 3rd highest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM10 in August 2024.,"Dangi Tola, Rajgir - BSPCB" 1728,2182,spatial_aggregation,Which state has the highest median PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest median PM2.5 value in February 2019.,Bihar 1729,2183,spatial_aggregation,Which station has the 3rd lowest average PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest average PM10 during August 2021?,"Kadri, Mangalore - KSPCB" 1730,2184,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM2.5 in December 2022.,Begusarai 1731,2185,spatial_aggregation,Which station has the 2nd lowest median PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM10 in April 2022?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1732,2186,spatial_aggregation,Which city has the 3rd highest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest median PM2.5 in July 2019.,Yamuna Nagar 1733,2187,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 75th percentile of PM10 for July 2023.,Arunachal Pradesh 1734,2188,spatial_aggregation,Which station has the 3rd highest median PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest median PM10 in January 2022?,"Mayaganj, Bhagalpur - BSPCB" 1735,2189,spatial_aggregation,Which city has the 3rd lowest average PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest average PM10 in September 2024.,Koppal 1736,2190,spatial_aggregation,Which city has the highest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in October 2020?,Panipat 1737,2191,spatial_aggregation,Which state has the lowest average PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest average PM2.5 in December 2024.,Mizoram 1738,2192,spatial_aggregation,Which city has the 3rd lowest average PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest average PM10 value in December 2020.,Aizawl 1739,2193,spatial_aggregation,Which state has the 3rd highest average PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest average PM10 during January 2020?,Assam 1740,2195,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM10 in February 2022?,"Lumpyngngad, Shillong - Meghalaya PCB" 1741,2197,spatial_aggregation,Which state has the lowest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM2.5 for March 2023.,Sikkim 1742,2198,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 75th percentile of PM2.5 in June 2024?,"Kumaran College, Tirupur - TNPCB" 1743,2200,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 75th percentile of PM2.5 in January 2023?,Arunachal Pradesh 1744,2201,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM10 in May 2022.,Gorakhpur 1745,2202,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest 75th percentile of PM10 value in January 2020.,Talcher 1746,2203,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 25th percentile of PM2.5 during April 2024?,Bareilly 1747,2204,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM10 in May 2019.,Ballabgarh 1748,2205,spatial_aggregation,Which state has the 2nd highest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM2.5 in September 2019?,Haryana 1749,2206,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 75th percentile of PM2.5 in December 2018.,"Nehru Nagar, Delhi - DPCC" 1750,2207,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 25th percentile of PM2.5 for February 2020.,Vapi 1751,2208,spatial_aggregation,Which state has the 3rd highest median PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest median PM2.5 in January 2024?,Chandigarh 1752,2209,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest 25th percentile of PM10 in February 2023.,Begusarai 1753,2211,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 75th percentile of PM10 in February 2024.,Sri Ganganagar 1754,2212,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest 75th percentile of PM10 value in April 2022.,Sikkim 1755,2213,spatial_aggregation,Which city has the highest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM10 during December 2021?,Faridabad 1756,2214,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 25th percentile of PM10 in August 2022.,Sikkim 1757,2215,spatial_aggregation,Which city has the 2nd highest average PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM10 in November 2023?,Hanumangarh 1758,2216,spatial_aggregation,Which city has the 3rd highest average PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest average PM2.5 in September 2022.,Kochi 1759,2217,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM2.5 for January 2019.,Patiala 1760,2219,spatial_aggregation,Which state has the highest average PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest average PM2.5 in April 2020.,Odisha 1761,2221,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 75th percentile of PM2.5 in April 2022.,Rajamahendravaram 1762,2222,spatial_aggregation,Which city has the highest average PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest average PM2.5 value in February 2018.,Talcher 1763,2223,spatial_aggregation,Which city has the highest 25th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 25th percentile of PM10 during September 2021?,Yamuna Nagar 1764,2226,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 75th percentile of PM2.5 in July 2018.,"New Collectorate, Baghpat - UPPCB" 1765,2228,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 25th percentile of PM2.5 in October 2024?,Kalaburagi 1766,2230,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM2.5 in February 2020?,"Manali Village, Chennai - TNPCB" 1767,2231,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 75th percentile of PM2.5 in September 2024.,Manipur 1768,2233,spatial_aggregation,Which city has the 3rd lowest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest median PM10 during February 2021?,Shillong 1769,2235,spatial_aggregation,Which city has the 3rd lowest average PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in January 2020?,Shillong 1770,2236,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest 75th percentile of PM10 in October 2018.,Jharkhand 1771,2237,spatial_aggregation,Which station has the 2nd highest average PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM10 for April 2018.,"Rohini, Delhi - DPCC" 1772,2239,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 25th percentile of PM2.5 in December 2019.,"Velachery Res. Area, Chennai - CPCB" 1773,2240,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM2.5 in April 2021?,"Devaraj Urs Badavane, Davanagere - KSPCB" 1774,2242,spatial_aggregation,Which state has the highest average PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest average PM10 value in March 2022.,Delhi 1775,2243,spatial_aggregation,Which state has the 3rd highest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest median PM10 during June 2019?,Haryana 1776,2245,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM10 in June 2022?,Jharkhand 1777,2246,spatial_aggregation,Which city has the highest average PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest average PM10 in December 2020.,Ghaziabad 1778,2248,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 75th percentile of PM10 in October 2023?,Sikkim 1779,2249,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM2.5 in January 2021.,"Vidayagiri, Bagalkot - KSPCB" 1780,2250,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM2.5 in January 2021?,Davanagere 1781,2251,spatial_aggregation,Which station has the 2nd highest median PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM2.5 in July 2024.,"GIDC, Nandesari - Nandesari Ind. Association" 1782,2253,spatial_aggregation,Which city has the highest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 25th percentile of PM10 during December 2019?,Panipat 1783,2254,spatial_aggregation,Which city has the lowest median PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest median PM10 in May 2023.,Nandesari 1784,2258,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM2.5 in November 2018?,Delhi 1785,2259,spatial_aggregation,Which state has the 3rd highest median PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest median PM10 in November 2019.,Haryana 1786,2260,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM10 in May 2023?,Damoh 1787,2262,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM10 value in April 2019.,Ghaziabad 1788,2263,spatial_aggregation,Which station has the highest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest 75th percentile of PM10 during December 2021?,"Anand Vihar, Delhi - DPCC" 1789,2264,spatial_aggregation,Which station has the 2nd lowest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest median PM10 in May 2024.,"Crescent University, Chengalpattu - TNPCB" 1790,2265,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in April 2019?,Palwal 1791,2266,spatial_aggregation,Which state has the 3rd lowest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM10 in July 2023.,Meghalaya 1792,2267,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 25th percentile of PM10 for June 2024.,Shillong 1793,2268,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM10 in August 2020?,Jharkhand 1794,2269,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 75th percentile of PM2.5 in July 2018.,Telangana 1795,2270,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 25th percentile of PM10 in March 2021?,Delhi 1796,2271,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 75th percentile of PM10 in February 2020.,"DTU, Delhi - CPCB" 1797,2272,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 75th percentile of PM2.5 value in January 2023.,Aizawl 1798,2276,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 75th percentile of PM2.5 in November 2023.,Gangtok 1799,2277,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile of PM10 for February 2022.,Meghalaya 1800,2278,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 75th percentile of PM2.5 in January 2021?,"Jahangirpuri, Delhi - DPCC" 1801,2279,spatial_aggregation,Which city has the lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest median PM2.5 in May 2018.,Thiruvananthapuram 1802,2280,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 25th percentile of PM2.5 in January 2020?,Noida 1803,2281,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in April 2023.,"Kunjaban, Agartala - Tripura SPCB" 1804,2282,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest 75th percentile of PM10 value in October 2020.,Kerala 1805,2283,spatial_aggregation,Which state has the highest median PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest median PM2.5 during February 2021?,Delhi 1806,2285,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM2.5 in December 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1807,2286,spatial_aggregation,Which city has the 3rd highest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest average PM10 in September 2018.,Bhiwadi 1808,2287,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM2.5 for December 2020.,Uttar Pradesh 1809,2288,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest 25th percentile of PM2.5 in June 2022?,Koppal 1810,2289,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest 75th percentile of PM10 in September 2024.,Greater Noida 1811,2291,spatial_aggregation,Which state has the 2nd lowest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest average PM10 in August 2024.,Meghalaya 1812,2292,spatial_aggregation,Which station has the 2nd highest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest average PM10 value in June 2018.,"Sirifort, Delhi - CPCB" 1813,2293,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during December 2024?,"PCBL Residential Complex, Durgapur - WBPCB" 1814,2296,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest 75th percentile of PM10 in April 2019.,Rajamahendravaram 1815,2297,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM10 for October 2020.,Kerala 1816,2298,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile of PM10 in December 2024?,"Municipal Corporation Office, Tirunelveli - TNPCB" 1817,2299,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 25th percentile of PM2.5 in September 2020.,Kerala 1818,2300,spatial_aggregation,Which state has the highest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in April 2018?,Delhi 1819,2302,spatial_aggregation,Which city has the 3rd lowest average PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest average PM10 value in March 2018.,Tirupati 1820,2304,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 75th percentile of PM10 in November 2024.,Vijayapura 1821,2305,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 75th percentile of PM2.5 in June 2023?,Aizawl 1822,2306,spatial_aggregation,Which state has the 2nd lowest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest average PM10 in July 2022.,Mizoram 1823,2307,spatial_aggregation,Which station has the highest median PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM10 for July 2019.,"Dwarka-Sector 8, Delhi - DPCC" 1824,2309,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 25th percentile of PM2.5 in April 2019.,"PWD Grounds, Vijayawada - APPCB" 1825,2310,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in February 2018?,Patiala 1826,2311,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest 75th percentile of PM10 in December 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1827,2312,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 75th percentile of PM2.5 value in February 2021.,Meghalaya 1828,2313,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM2.5 during February 2021?,Moradabad 1829,2314,spatial_aggregation,Which station has the 3rd lowest median PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest median PM10 in November 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1830,2316,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station that had the 3rd highest 25th percentile of PM2.5 in November 2020.,"Bawana, Delhi - DPCC" 1831,2318,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM2.5 in July 2022?,Sikkim 1832,2319,spatial_aggregation,Which station has the lowest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest average PM2.5 in July 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 1833,2320,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in August 2018?,"Central University, Hyderabad - TSPCB" 1834,2321,spatial_aggregation,Which city has the 3rd highest median PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM2.5 in November 2018.,Baghpat 1835,2322,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 25th percentile of PM2.5 value in September 2018.,Rajasthan 1836,2323,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest 75th percentile of PM10 during December 2019?,"Lumpyngngad, Shillong - Meghalaya PCB" 1837,2324,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest 25th percentile of PM2.5 in March 2024.,"NSIT Dwarka, Delhi - CPCB" 1838,2325,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 25th percentile of PM2.5 in November 2019?,Delhi 1839,2326,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 75th percentile of PM10 in March 2018.,Talcher 1840,2327,spatial_aggregation,Which city has the 2nd highest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM10 for November 2021.,Bihar Sharif 1841,2330,spatial_aggregation,Which station has the highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM10 in July 2022?,"D M Colony, Bihar Sharif - BSPCB" 1842,2331,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 25th percentile of PM10 in July 2020.,"Sector 11, Faridabad - HSPCB" 1843,2332,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 25th percentile of PM10 value in May 2021.,"Brahmagiri, Udupi - KSPCB" 1844,2333,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest 75th percentile of PM2.5 during September 2023?,Surat 1845,2334,spatial_aggregation,Which state has the 2nd highest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest average PM2.5 in August 2018.,Haryana 1846,2335,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in July 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1847,2336,spatial_aggregation,Which state has the lowest average PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest average PM10 in June 2020.,Mizoram 1848,2337,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 25th percentile of PM2.5 for February 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 1849,2338,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 75th percentile of PM10 in April 2024?,"Semmandalam, Cuddalore - TNPCB" 1850,2339,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 25th percentile of PM10 in May 2020.,Meghalaya 1851,2340,spatial_aggregation,Which city has the 3rd highest average PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in February 2022?,Durgapur 1852,2341,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 25th percentile of PM10 in April 2022.,Dharuhera 1853,2342,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest 75th percentile of PM2.5 value in August 2018.,Telangana 1854,2343,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest 25th percentile of PM2.5 during March 2023?,Byrnihat 1855,2345,spatial_aggregation,Which station has the 3rd highest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest average PM2.5 in October 2018?,"Rohini, Delhi - DPCC" 1856,2346,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 75th percentile of PM10 in April 2018.,Kolkata 1857,2347,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 25th percentile of PM10 for September 2024.,Samastipur 1858,2348,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM10 in October 2020?,Meghalaya 1859,2350,spatial_aggregation,Which city has the highest median PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM2.5 in November 2023?,Hanumangarh 1860,2351,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM2.5 in May 2024.,"Crescent University, Chengalpattu - TNPCB" 1861,2352,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest 75th percentile of PM10 value in October 2022.,"Burari Crossing, Delhi - IMD" 1862,2353,spatial_aggregation,Which city has the 2nd lowest median PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd lowest median PM10 during July 2020?,Aizawl 1863,2354,spatial_aggregation,Which city has the lowest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest average PM10 in November 2021.,Shillong 1864,2355,spatial_aggregation,Which station has the highest 75th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM10 in October 2022?,"Anand Vihar, Delhi - DPCC" 1865,2356,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 25th percentile of PM2.5 in April 2024.,"Civil Lines, Bareilly - UPPCB" 1866,2357,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 75th percentile of PM10 for April 2022.,Bihar 1867,2359,spatial_aggregation,Which state has the lowest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM2.5 in December 2018.,Karnataka 1868,2360,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in July 2022?,"Kareemganj, Gaya - BSPCB" 1869,2361,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest 25th percentile of PM2.5 in August 2018.,"Police Commissionerate, Jaipur - RSPCB" 1870,2363,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest median PM2.5 during October 2022?,"Zero Point GICI, Gangtok - SSPCB" 1871,2365,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM2.5 in February 2020?,Tamil Nadu 1872,2366,spatial_aggregation,Which state has the lowest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest average PM2.5 in March 2020.,Chandigarh 1873,2367,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 25th percentile of PM10 for July 2021.,"Dangi Tola, Rajgir - BSPCB" 1874,2369,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 75th percentile of PM10 in May 2018.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 1875,2371,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 75th percentile of PM10 in August 2022.,Gangtok 1876,2372,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 75th percentile of PM2.5 value in May 2023.,"Zero Point GICI, Gangtok - SSPCB" 1877,2374,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest 25th percentile of PM10 in July 2019.,Ahmedabad 1878,2376,spatial_aggregation,Which station has the 3rd highest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station that had the 3rd highest average PM2.5 in July 2022.,"Karve Road, Pune - MPCB" 1879,2377,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 75th percentile of PM10 for December 2021.,"Shadipur, Delhi - CPCB" 1880,2378,spatial_aggregation,Which state has the 3rd highest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest median PM2.5 in May 2018?,Haryana 1881,2379,spatial_aggregation,Which city has the lowest median PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest median PM2.5 in August 2021.,Aizawl 1882,2380,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM2.5 in November 2023?,"Wazirpur, Delhi - DPCC" 1883,2381,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in March 2021.,Bihar 1884,2382,spatial_aggregation,Which station has the 2nd highest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest median PM10 value in April 2019.,"Loni, Ghaziabad - UPPCB" 1885,2384,spatial_aggregation,Which station has the 2nd highest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM10 in November 2021.,"Anand Vihar, Delhi - DPCC" 1886,2385,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in December 2024?,"Trivenidevi Bhalotia College, Asansol - WBPCB" 1887,2386,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest 75th percentile of PM10 in July 2024.,Meghalaya 1888,2387,spatial_aggregation,Which station has the 2nd highest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM2.5 for February 2023.,"Sector-19A Nerul, Navi Mumbai - IITM" 1889,2388,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile of PM2.5 in October 2019?,"Tirumala, Tirupati - APPCB" 1890,2389,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 75th percentile of PM2.5 in December 2020.,Kanpur 1891,2390,spatial_aggregation,Which station has the lowest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in January 2023?,"Deen Dayal Nagar, Sagar - MPPCB" 1892,2391,spatial_aggregation,Which city has the lowest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest median PM2.5 in July 2019.,Bathinda 1893,2393,spatial_aggregation,Which city has the 3rd highest median PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest median PM10 during May 2020?,Chennai 1894,2394,spatial_aggregation,Which city has the highest average PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest average PM10 in December 2019.,Panipat 1895,2395,spatial_aggregation,Which city has the highest average PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest average PM10 in May 2019?,Ghaziabad 1896,2396,spatial_aggregation,Which city has the highest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest average PM2.5 in July 2022.,Saharsa 1897,2398,spatial_aggregation,Which city has the lowest median PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest median PM10 in June 2023?,Silchar 1898,2399,spatial_aggregation,Which state has the 2nd highest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM2.5 in March 2019.,Bihar 1899,2401,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 25th percentile of PM10 in January 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1900,2402,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 75th percentile of PM10 value in February 2019.,Karnataka 1901,2403,spatial_aggregation,Which state has the 3rd lowest average PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest average PM10 during January 2023?,Mizoram 1902,2405,spatial_aggregation,Which station has the 3rd highest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in April 2019?,"Talkatora District Industries Center, Lucknow - CPCB" 1903,2406,spatial_aggregation,Which station has the 2nd highest median PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest median PM2.5 in September 2018.,"NISE Gwal Pahari, Gurugram - IMD" 1904,2407,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 75th percentile of PM10 for April 2023.,Byrnihat 1905,2408,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM2.5 in May 2021?,Rajasthan 1906,2409,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 75th percentile of PM10 in November 2022.,"Brahmagiri, Udupi - KSPCB" 1907,2411,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in October 2019.,Sirsa 1908,2413,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest 25th percentile of PM10 during April 2023?,"DRCC Anandpur, Begusarai - BSPCB" 1909,2414,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM2.5 in November 2022.,Haryana 1910,2416,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 25th percentile of PM2.5 in August 2022.,Ambala 1911,2417,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for August 2018.,"NSIT Dwarka, Delhi - CPCB" 1912,2418,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 75th percentile of PM2.5 in May 2018?,"Kendriya Vidyalaya, Lucknow - CPCB" 1913,2419,spatial_aggregation,Which city has the 3rd highest average PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest average PM2.5 in January 2018.,Lucknow 1914,2423,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 25th percentile of PM10 during August 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1915,2424,spatial_aggregation,Which station has the 3rd highest average PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest average PM10 in September 2019.,"Dwarka-Sector 8, Delhi - DPCC" 1916,2425,spatial_aggregation,Which city has the highest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest average PM10 in July 2019?,Sirsa 1917,2426,spatial_aggregation,Which state has the highest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest median PM10 in June 2022.,Delhi 1918,2427,spatial_aggregation,Which city has the 2nd highest average PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM2.5 for August 2024.,Dhanbad 1919,2428,spatial_aggregation,Which state has the 2nd lowest average PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest average PM10 in October 2020?,Meghalaya 1920,2429,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM2.5 in December 2023.,Mizoram 1921,2430,spatial_aggregation,Which station has the lowest average PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM10 in April 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1922,2432,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 25th percentile of PM10 value in July 2019.,Chandrapur 1923,2433,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM2.5 during January 2019?,Punjab 1924,2434,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 75th percentile of PM2.5 in August 2024.,Manipur 1925,2436,spatial_aggregation,Which city has the lowest average PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest average PM10 in December 2022.,Udupi 1926,2437,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM2.5 for August 2020.,"Solapur, Solapur - MPCB" 1927,2438,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest 75th percentile of PM10 in August 2024?,"Lodhi Road, Delhi - IITM" 1928,2439,spatial_aggregation,Which city has the lowest average PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest average PM2.5 in May 2020.,Eloor 1929,2440,spatial_aggregation,Which state has the lowest average PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest average PM10 in December 2020?,Meghalaya 1930,2441,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 25th percentile of PM10 in October 2023.,Himachal Pradesh 1931,2443,spatial_aggregation,Which station has the 2nd highest median PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest median PM10 during June 2023?,"Chandni Chowk, Delhi - IITM" 1932,2445,spatial_aggregation,Which state has the 2nd lowest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest average PM10 in November 2020?,Mizoram 1933,2446,spatial_aggregation,Which state has the 3rd lowest average PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM10 in April 2020.,Chandigarh 1934,2447,spatial_aggregation,Which state has the 2nd highest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest average PM2.5 for February 2020.,Assam 1935,2448,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM10 in September 2019?,"Udyogamandal, Eloor - Kerala PCB" 1936,2449,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest 25th percentile of PM10 in November 2022.,Himachal Pradesh 1937,2450,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM10 in March 2021?,Kolar 1938,2451,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 25th percentile of PM2.5 in August 2020.,Nandesari 1939,2452,spatial_aggregation,Which city has the lowest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest median PM10 value in September 2024.,Maihar 1940,2454,spatial_aggregation,Which station has the 2nd highest median PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest median PM2.5 in December 2024.,"Central Academy for SFS, Byrnihat - PCBA" 1941,2456,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 75th percentile of PM2.5 in February 2019.,Uttar Pradesh 1942,2457,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM2.5 for May 2024.,Sikkim 1943,2459,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 25th percentile of PM10 in July 2023.,Sikkim 1944,2462,spatial_aggregation,Which station has the 2nd highest average PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest average PM10 value in January 2021.,"Talkatora District Industries Center, Lucknow - CPCB" 1945,2463,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest 75th percentile of PM10 during October 2021?,Uttar Pradesh 1946,2464,spatial_aggregation,Which state has the highest average PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest average PM10 in April 2020.,Uttar Pradesh 1947,2465,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 25th percentile of PM2.5 in October 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1948,2466,spatial_aggregation,Which city has the lowest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest average PM2.5 in February 2024.,Satna 1949,2467,spatial_aggregation,Which station has the 3rd lowest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM10 for March 2019.,"PWD Grounds, Vijayawada - APPCB" 1950,2468,spatial_aggregation,Which station has the lowest median PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest median PM2.5 in December 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 1951,2469,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 25th percentile of PM10 in February 2022.,Jharkhand 1952,2471,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM2.5 in May 2021.,Tamil Nadu 1953,2472,spatial_aggregation,Which state has the 3rd highest median PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest median PM2.5 value in September 2024.,Assam 1954,2473,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest 25th percentile of PM10 during March 2022?,Nandesari 1955,2474,spatial_aggregation,Which state has the 3rd highest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM10 in February 2024.,Assam 1956,2475,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM2.5 in May 2023?,"DRCC Anandpur, Begusarai - BSPCB" 1957,2476,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM10 in October 2023.,Arunachal Pradesh 1958,2477,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 25th percentile of PM2.5 for January 2022.,Aizawl 1959,2478,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 75th percentile of PM2.5 in January 2018?,Bihar 1960,2479,spatial_aggregation,Which station has the highest 25th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 25th percentile of PM10 in September 2021.,"Wazirpur, Delhi - DPCC" 1961,2480,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM2.5 in December 2023?,Eloor 1962,2482,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 75th percentile of PM10 value in August 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1963,2483,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 75th percentile of PM10 during November 2020?,Shillong 1964,2484,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 25th percentile of PM10 in May 2020.,Tamil Nadu 1965,2485,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in February 2019?,"Shadipur, Delhi - CPCB" 1966,2486,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest 75th percentile of PM2.5 in October 2021.,"Nathu Colony, Ballabgarh - HSPCB" 1967,2487,spatial_aggregation,Which state has the highest average PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 for October 2023.,Delhi 1968,2488,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest 25th percentile of PM2.5 in August 2018?,Satna 1969,2489,spatial_aggregation,Which state has the lowest median PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM10 in January 2020.,Meghalaya 1970,2490,spatial_aggregation,Which station has the 3rd highest median PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in April 2020?,"Suryakiran Bhawan NCL, Singrauli - MPPCB" 1971,2491,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM2.5 in November 2022.,Bhilai 1972,2493,spatial_aggregation,Which state has the lowest median PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest median PM10 during April 2018?,Kerala 1973,2494,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 25th percentile of PM2.5 in February 2018.,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 1974,2496,spatial_aggregation,Which city has the 3rd lowest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest average PM10 in October 2021.,Madikeri 1975,2497,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM2.5 for April 2018.,Bihar 1976,2498,spatial_aggregation,Which city has the 3rd highest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest average PM10 in July 2023?,Byrnihat 1977,2499,spatial_aggregation,Which station has the 2nd lowest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM10 in December 2024.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 1978,2500,spatial_aggregation,Which city has the highest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM10 in February 2020?,Ballabgarh 1979,2501,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 75th percentile of PM2.5 in August 2020.,"GIDC, Nandesari - Nandesari Ind. Association" 1980,2502,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 25th percentile of PM10 value in September 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 1981,2503,spatial_aggregation,Which city has the highest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest median PM2.5 during November 2024?,Delhi 1982,2504,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest 75th percentile of PM2.5 in April 2022.,Mizoram 1983,2505,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in December 2019?,"Udyogamandal, Eloor - Kerala PCB" 1984,2506,spatial_aggregation,Which state has the highest 75th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 75th percentile of PM10 in March 2019.,Uttar Pradesh 1985,2507,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM2.5 for October 2019.,Delhi 1986,2508,spatial_aggregation,Which station has the 2nd highest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median PM10 in October 2024?,"Mundka, Delhi - DPCC" 1987,2509,spatial_aggregation,Which state has the highest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 25th percentile of PM10 in August 2020.,Odisha 1988,2510,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM10 in August 2021?,"Anand Vihar, Delhi - DPCC" 1989,2511,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 75th percentile of PM10 in March 2018.,Vijayawada 1990,2512,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 25th percentile of PM10 value in January 2021.,Meghalaya 1991,2513,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM10 during January 2018?,Delhi 1992,2514,spatial_aggregation,Which station has the 3rd highest median PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest median PM10 in April 2022.,"Suryakiran Bhawan NCL, Singrauli - MPPCB" 1993,2515,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM10 in April 2022?,Uttar Pradesh 1994,2516,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest 25th percentile of PM10 in September 2022.,Sikkim 1995,2517,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 25th percentile of PM10 for September 2024.,"Old City, Sri Ganganagar - RSPCB" 1996,2518,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM2.5 in March 2018?,Brajrajnagar 1997,2519,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the 2nd highest 75th percentile of PM10 in April 2021.,"Mundka, Delhi - DPCC" 1998,2520,spatial_aggregation,Which state has the highest 75th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 75th percentile of PM10 in February 2018?,Delhi 1999,2524,spatial_aggregation,Which state has the lowest average PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM10 in August 2019.,Kerala 2000,2525,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM2.5 in November 2018?,Chandrapur 2001,2526,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest 75th percentile of PM2.5 in January 2020.,Bihar 2002,2527,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 75th percentile of PM10 for March 2022.,Mangalore 2003,2528,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM2.5 in March 2022?,Vijayapura 2004,2529,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile of PM2.5 in March 2019.,Assam 2005,2530,spatial_aggregation,Which station has the lowest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in September 2020?,"Sanathnagar, Hyderabad - TSPCB" 2006,2531,spatial_aggregation,Which station has the highest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 25th percentile of PM10 in January 2020.,"Ghusuri, Howrah - WBPCB" 2007,2532,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest 25th percentile of PM2.5 value in May 2023.,Jharkhand 2008,2533,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest 75th percentile of PM2.5 during October 2023?,Byrnihat 2009,2534,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM2.5 in December 2019.,"Udyogamandal, Eloor - Kerala PCB" 2010,2536,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM2.5 in August 2021.,Arunachal Pradesh 2011,2537,spatial_aggregation,Which state has the 2nd lowest average PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM10 for February 2022.,Chhattisgarh 2012,2539,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest 25th percentile of PM2.5 in August 2018.,"Vikas Sadan, Gurugram - HSPCB" 2013,2541,spatial_aggregation,Which state has the lowest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in January 2020.,Meghalaya 2014,2542,spatial_aggregation,Which city has the highest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest average PM2.5 value in March 2019.,Varanasi 2015,2543,spatial_aggregation,Which state has the highest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest 75th percentile of PM10 during April 2019?,Uttar Pradesh 2016,2544,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in March 2022.,Bihar 2017,2546,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM2.5 in January 2018.,Telangana 2018,2547,spatial_aggregation,Which station has the 2nd lowest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest median PM10 for May 2019.,"Tamaka Ind. Area, Kolar - KSPCB" 2019,2548,spatial_aggregation,Which city has the highest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest average PM2.5 in September 2020?,Bhiwadi 2020,2549,spatial_aggregation,Which city has the highest median PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM10 in January 2022.,Saharsa 2021,2550,spatial_aggregation,Which city has the 2nd lowest median PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM10 in February 2019?,Maihar 2022,2551,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest median PM2.5 in October 2019.,"Tirumala, Tirupati - APPCB" 2023,2552,spatial_aggregation,Which station has the highest median PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest median PM10 value in January 2020.,"Ghusuri, Howrah - WBPCB" 2024,2553,spatial_aggregation,Which state has the 2nd highest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM2.5 during January 2019?,Delhi 2025,2554,spatial_aggregation,Which state has the lowest average PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM2.5 in July 2019.,Kerala 2026,2555,spatial_aggregation,Which city has the highest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM2.5 in November 2019?,Kanpur 2027,2556,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 75th percentile of PM2.5 in October 2020.,Madikeri 2028,2557,spatial_aggregation,Which station has the 3rd highest median PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 for August 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 2029,2558,spatial_aggregation,Which state has the lowest average PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest average PM2.5 in June 2020?,Mizoram 2030,2559,spatial_aggregation,Which city has the 3rd lowest median PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest median PM10 in July 2024.,Gangtok 2031,2561,spatial_aggregation,Which station has the 3rd highest median PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest median PM2.5 in July 2023.,"Central Academy for SFS, Byrnihat - PCBA" 2032,2563,spatial_aggregation,Which state has the 3rd highest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest median PM2.5 during March 2023?,Tripura 2033,2564,spatial_aggregation,Which state has the lowest average PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM2.5 in April 2023.,Sikkim 2034,2565,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in August 2018?,Solapur 2035,2566,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM2.5 in October 2018.,Maharashtra 2036,2567,spatial_aggregation,Which station has the highest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest average PM10 for November 2021.,"Sector 11, Faridabad - HSPCB" 2037,2568,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM2.5 in August 2022?,"GIDC, Nandesari - Nandesari Ind. Association" 2038,2569,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM2.5 in December 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2039,2572,spatial_aggregation,Which city has the 3rd lowest average PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest average PM10 value in March 2020.,Maihar 2040,2573,spatial_aggregation,Which state has the 2nd highest average PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM2.5 during March 2021?,Delhi 2041,2574,spatial_aggregation,Which state has the highest average PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest average PM2.5 in January 2018.,Uttar Pradesh 2042,2576,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest 75th percentile of PM10 in July 2019.,Chamarajanagar 2043,2577,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 25th percentile of PM2.5 for January 2024.,Delhi 2044,2578,spatial_aggregation,Which station has the highest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 75th percentile of PM10 in March 2021?,"Loni, Ghaziabad - UPPCB" 2045,2579,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 25th percentile of PM2.5 in February 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2046,2580,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM10 in May 2019?,"Manali Village, Chennai - TNPCB" 2047,2581,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM2.5 in January 2019.,"Model Town, Patiala - PPCB" 2048,2582,spatial_aggregation,Which city has the lowest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest average PM10 value in September 2023.,Udupi 2049,2583,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest 25th percentile of PM10 during October 2020?,"DTU, Delhi - CPCB" 2050,2584,spatial_aggregation,Which city has the 2nd highest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM10 in April 2022.,Bihar Sharif 2051,2585,spatial_aggregation,Which station has the 3rd lowest median PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM10 in September 2020?,"Vinoba Nagara, Shivamogga - KSPCB" 2052,2586,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 25th percentile of PM2.5 in July 2022.,"DM College of Science, Imphal - Manipur PCB" 2053,2587,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 75th percentile of PM2.5 for July 2018.,Bhiwadi 2054,2588,spatial_aggregation,Which city has the highest median PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest median PM10 in February 2019?,Talcher 2055,2589,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 75th percentile of PM2.5 in February 2021.,"Devaraj Urs Badavane, Davanagere - KSPCB" 2056,2590,spatial_aggregation,Which city has the 3rd lowest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in March 2024?,Maihar 2057,2592,spatial_aggregation,Which station has the 3rd highest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest median PM10 value in May 2019.,"Loni, Ghaziabad - UPPCB" 2058,2593,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest 25th percentile of PM2.5 during August 2023?,"RVCE-Mailasandra, Bengaluru - KSPCB" 2059,2594,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM10 in August 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2060,2595,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM10 in February 2021?,"Tamaka Ind. Area, Kolar - KSPCB" 2061,2596,spatial_aggregation,Which city has the lowest average PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest average PM10 in July 2021.,Shillong 2062,2597,spatial_aggregation,Which station has the 3rd highest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM10 for December 2024.,"Mundka, Delhi - DPCC" 2063,2598,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 75th percentile of PM2.5 in October 2018?,Kerala 2064,2599,spatial_aggregation,Which state has the lowest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM10 in May 2021.,Meghalaya 2065,2600,spatial_aggregation,Which state has the 3rd lowest average PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest average PM10 in February 2021?,Jharkhand 2066,2601,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 75th percentile of PM2.5 in March 2024.,"RVCE-Mailasandra, Bengaluru - KSPCB" 2067,2602,spatial_aggregation,Which station has the 2nd lowest average PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest average PM10 value in November 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2068,2603,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 25th percentile of PM10 during November 2022?,Ernakulam 2069,2604,spatial_aggregation,Which state has the highest average PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest average PM2.5 in August 2023.,Jharkhand 2070,2607,spatial_aggregation,Which city has the 2nd highest average PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM10 for October 2023.,Hanumangarh 2071,2608,spatial_aggregation,Which state has the 2nd highest median PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM2.5 in September 2024?,Delhi 2072,2609,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest 25th percentile of PM2.5 in July 2024.,Delhi 2073,2610,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM2.5 in December 2023?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2074,2611,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 25th percentile of PM10 in July 2020.,Aizawl 2075,2612,spatial_aggregation,Which state has the highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest median PM2.5 value in January 2022.,Delhi 2076,2614,spatial_aggregation,Which city has the 3rd lowest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest median PM10 in August 2024.,Shillong 2077,2615,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM2.5 in April 2022?,Singrauli 2078,2616,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 75th percentile of PM2.5 in January 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 2079,2618,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM10 in September 2020?,Kerala 2080,2619,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 75th percentile of PM10 in April 2020.,Chandigarh 2081,2620,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in March 2023?,Byrnihat 2082,2622,spatial_aggregation,Which city has the lowest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest median PM10 value in August 2024.,Koppal 2083,2623,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM2.5 during October 2019?,Bihar 2084,2624,spatial_aggregation,Which station has the highest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM10 in August 2024.,"Maguda Nagar, Indore - IMC" 2085,2627,spatial_aggregation,Which station has the highest 25th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM10 for October 2022.,"Anand Vihar, Delhi - DPCC" 2086,2628,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM10 in June 2021?,"Panchal Nagar, Gadag - KSPCB" 2087,2629,spatial_aggregation,Which state has the 3rd lowest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest average PM10 in February 2020.,Andhra Pradesh 2088,2630,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 75th percentile of PM10 in June 2024?,Meghalaya 2089,2631,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest median PM2.5 in December 2022.,Arunachal Pradesh 2090,2634,spatial_aggregation,Which city has the 2nd highest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM10 in April 2023.,Begusarai 2091,2635,spatial_aggregation,Which state has the highest average PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM10 in August 2018?,Jharkhand 2092,2638,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile of PM2.5 in June 2019?,Lucknow 2093,2639,spatial_aggregation,Which state has the 2nd lowest median PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM10 in January 2021.,Mizoram 2094,2640,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM10 in January 2022?,"Lumpyngngad, Shillong - Meghalaya PCB" 2095,2641,spatial_aggregation,Which station has the lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest median PM2.5 in May 2018.,"Kendriya Vidyalaya, Lucknow - CPCB" 2096,2642,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest median PM2.5 value in February 2020.,Eloor 2097,2643,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM2.5 during September 2022?,Delhi 2098,2645,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in June 2018?,"Sanegurava Halli, Bengaluru - KSPCB" 2099,2646,spatial_aggregation,Which city has the highest average PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest average PM10 in December 2023.,Begusarai 2100,2647,spatial_aggregation,Which city has the lowest average PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest average PM2.5 for November 2019.,Eloor 2101,2648,spatial_aggregation,Which state has the highest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest median PM10 in September 2022?,Himachal Pradesh 2102,2649,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 25th percentile of PM10 in June 2018.,"ICRISAT Patancheru, Hyderabad - TSPCB" 2103,2650,spatial_aggregation,Which state has the 2nd lowest median PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest median PM10 in January 2018?,Kerala 2104,2651,spatial_aggregation,Which station has the 3rd lowest average PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest average PM10 in May 2022.,"Brahmagiri, Udupi - KSPCB" 2105,2652,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 25th percentile of PM10 value in May 2020.,"ITO, Delhi - CPCB" 2106,2653,spatial_aggregation,Which state has the lowest average PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest average PM2.5 during June 2023?,Sikkim 2107,2654,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest 75th percentile of PM2.5 in September 2019.,Rajamahendravaram 2108,2655,spatial_aggregation,Which state has the 2nd highest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM2.5 in April 2019?,Delhi 2109,2656,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM2.5 in August 2019.,Jharkhand 2110,2657,spatial_aggregation,Which station has the 3rd highest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 for July 2020.,"Sector 11, Faridabad - HSPCB" 2111,2658,spatial_aggregation,Which city has the highest 75th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile of PM10 in January 2024?,Patna 2112,2659,spatial_aggregation,Which station has the lowest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest average PM2.5 in October 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2113,2660,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM10 in April 2023?,"Brahmagiri, Udupi - KSPCB" 2114,2661,spatial_aggregation,Which city has the highest average PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest average PM10 in June 2021.,Sonipat 2115,2662,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 25th percentile of PM10 value in November 2022.,Udupi 2116,2663,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 75th percentile of PM10 during September 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2117,2664,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 75th percentile of PM2.5 in September 2018.,"Tirumala, Tirupati - APPCB" 2118,2665,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM10 in November 2024?,Nagapattinam 2119,2666,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 25th percentile of PM10 in September 2022.,Ambala 2120,2667,spatial_aggregation,Which station has the 3rd highest average PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest average PM2.5 for January 2021.,"Nehru Nagar, Delhi - DPCC" 2121,2668,spatial_aggregation,Which city has the highest average PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest average PM2.5 in July 2023?,Nandesari 2122,2670,spatial_aggregation,Which city has the lowest median PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM10 in July 2024?,Koppal 2123,2671,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 75th percentile of PM10 in June 2023.,Arunachal Pradesh 2124,2672,spatial_aggregation,Which station has the 2nd highest median PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest median PM2.5 value in April 2024.,"Muradpur, Patna - BSPCB" 2125,2673,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest 25th percentile of PM2.5 during December 2022?,Arunachal Pradesh 2126,2674,spatial_aggregation,Which state has the 3rd highest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest median PM10 in January 2023.,Himachal Pradesh 2127,2675,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 25th percentile of PM10 in September 2023?,"Brahmagiri, Udupi - KSPCB" 2128,2676,spatial_aggregation,Which station has the highest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest average PM10 in April 2019.,"Mundka, Delhi - DPCC" 2129,2678,spatial_aggregation,Which city has the 3rd lowest median PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median PM10 in December 2021?,Aizawl 2130,2681,spatial_aggregation,Which state has the 2nd highest median PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest median PM10 in February 2019.,Delhi 2131,2683,spatial_aggregation,Which station has the 2nd highest average PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest average PM2.5 during July 2019?,"F-Block, Sirsa - HSPCB" 2132,2684,spatial_aggregation,Which station has the 2nd highest average PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM10 in February 2019.,"Wazirpur, Delhi - DPCC" 2133,2685,spatial_aggregation,Which state has the highest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in July 2019?,Delhi 2134,2686,spatial_aggregation,Which city has the 2nd lowest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest median PM10 in September 2024.,Gangtok 2135,2690,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 25th percentile of PM2.5 in November 2019?,"Udyogamandal, Eloor - Kerala PCB" 2136,2691,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in September 2018.,Uttar Pradesh 2137,2692,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest 25th percentile of PM2.5 value in January 2024.,Bhagalpur 2138,2693,spatial_aggregation,Which state has the 2nd highest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM10 during October 2021?,Uttar Pradesh 2139,2694,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 75th percentile of PM2.5 in June 2024.,Puducherry 2140,2695,spatial_aggregation,Which state has the 2nd highest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM2.5 in November 2020?,Uttar Pradesh 2141,2696,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 25th percentile of PM2.5 in March 2024.,Nandesari 2142,2698,spatial_aggregation,Which station has the lowest median PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest median PM2.5 in June 2021?,"Ratanpura, Rupnagar - Ambuja Cements" 2143,2699,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest 75th percentile of PM2.5 in August 2021.,Delhi 2144,2700,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest median PM2.5 in March 2024?,Sikkim 2145,2702,spatial_aggregation,Which state has the 3rd lowest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest average PM10 value in February 2018.,Andhra Pradesh 2146,2703,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest 75th percentile of PM2.5 during May 2021?,Delhi 2147,2704,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest 25th percentile of PM10 in November 2023.,Gangtok 2148,2705,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM2.5 in November 2021?,"Loni, Ghaziabad - UPPCB" 2149,2706,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest 75th percentile of PM10 in May 2018.,Madhya Pradesh 2150,2708,spatial_aggregation,Which city has the 2nd lowest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM10 in July 2019?,Chamarajanagar 2151,2709,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest average PM2.5 in June 2021.,Koppal 2152,2710,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest median PM2.5 in November 2019?,Kerala 2153,2711,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM2.5 in October 2018.,Nagpur 2154,2712,spatial_aggregation,Which city has the 2nd lowest median PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest median PM10 value in May 2018.,Jorapokhar 2155,2714,spatial_aggregation,Which city has the 2nd highest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM10 in February 2020.,Panipat 2156,2715,spatial_aggregation,Which station has the 2nd highest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM10 in May 2020?,"ITO, Delhi - CPCB" 2157,2716,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest 25th percentile of PM10 in September 2023.,Udupi 2158,2717,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 75th percentile of PM2.5 for June 2018.,Gurugram 2159,2718,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest average PM2.5 in April 2024?,Sikkim 2160,2719,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest median PM2.5 in August 2020.,Kalaburagi 2161,2720,spatial_aggregation,Which state has the 2nd highest average PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM10 in June 2021?,Haryana 2162,2721,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 75th percentile of PM2.5 in May 2023.,Tripura 2163,2722,spatial_aggregation,Which station has the 3rd highest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest median PM2.5 value in December 2023.,"Anand Vihar, Delhi - DPCC" 2164,2723,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 75th percentile of PM2.5 during November 2021?,"Rohini, Delhi - DPCC" 2165,2724,spatial_aggregation,Which city has the highest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 in July 2021.,Sonipat 2166,2725,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM10 in January 2020?,"Urban, Chamarajanagar - KSPCB" 2167,2727,spatial_aggregation,Which city has the 2nd highest median PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM2.5 for August 2021.,Manesar 2168,2728,spatial_aggregation,Which station has the highest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest average PM2.5 in March 2019?,"Talkatora District Industries Center, Lucknow - CPCB" 2169,2729,spatial_aggregation,Which state has the highest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 25th percentile of PM10 in September 2024.,Himachal Pradesh 2170,2730,spatial_aggregation,Which city has the 3rd lowest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in May 2020?,Coimbatore 2171,2731,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 75th percentile of PM2.5 in February 2020.,Andhra Pradesh 2172,2732,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 25th percentile of PM10 value in August 2019.,Kerala 2173,2735,spatial_aggregation,Which state has the 2nd lowest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest average PM10 in January 2024?,Mizoram 2174,2737,spatial_aggregation,Which station has the highest average PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest average PM2.5 for August 2022.,"Karve Road, Pune - MPCB" 2175,2740,spatial_aggregation,Which city has the 2nd highest median PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in May 2022?,Sonipat 2176,2742,spatial_aggregation,Which city has the lowest average PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest average PM2.5 value in March 2024.,Varanasi 2177,2743,spatial_aggregation,Which city has the highest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest 75th percentile of PM10 during October 2021?,Bhiwadi 2178,2744,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 75th percentile of PM10 in February 2019.,"Sanegurava Halli, Bengaluru - KSPCB" 2179,2745,spatial_aggregation,Which station has the lowest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM10 in April 2023?,"Brahmagiri, Udupi - KSPCB" 2180,2746,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest 75th percentile of PM2.5 in October 2020.,Bhiwadi 2181,2747,spatial_aggregation,Which state has the 3rd lowest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest median PM10 for October 2020.,Kerala 2182,2748,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM2.5 in February 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2183,2749,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 25th percentile of PM10 in January 2021.,Mizoram 2184,2752,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 75th percentile of PM2.5 value in January 2019.,"Anand Vihar, Delhi - DPCC" 2185,2755,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM2.5 in November 2023?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2186,2757,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest median PM2.5 for July 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2187,2759,spatial_aggregation,Which state has the 3rd highest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest average PM10 in March 2019.,Delhi 2188,2760,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM10 in November 2021?,"Sector 11, Faridabad - HSPCB" 2189,2761,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in November 2024.,"Municipal Corporation Office, Tirunelveli - TNPCB" 2190,2762,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest 75th percentile of PM2.5 value in April 2022.,Delhi 2191,2763,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest 25th percentile of PM10 during December 2019?,"Dwarka-Sector 8, Delhi - DPCC" 2192,2765,spatial_aggregation,Which state has the 3rd lowest average PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest average PM10 in October 2024?,Meghalaya 2193,2767,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest average PM2.5 for June 2022.,Haveri 2194,2768,spatial_aggregation,Which state has the lowest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest median PM10 in June 2022?,Jharkhand 2195,2770,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 75th percentile of PM2.5 in June 2018?,"Pimpleshwar Mandir, Thane - MPCB" 2196,2771,spatial_aggregation,Which station has the 3rd highest median PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest median PM10 in December 2019.,"Jahangirpuri, Delhi - DPCC" 2197,2772,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest 25th percentile of PM2.5 value in March 2023.,Begusarai 2198,2773,spatial_aggregation,Which station has the highest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest average PM10 during January 2024?,"Muradpur, Patna - BSPCB" 2199,2774,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 75th percentile of PM2.5 in July 2021.,"Diwator Nagar, Koppal - KSPCB" 2200,2775,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in February 2021?,"Sector-116, Noida - UPPCB" 2201,2776,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 75th percentile of PM2.5 in September 2020.,Kozhikode 2202,2777,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM2.5 for November 2020.,Delhi 2203,2778,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 75th percentile of PM2.5 in May 2024?,Faridabad 2204,2779,spatial_aggregation,Which state has the 3rd highest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest average PM10 in November 2024.,Chandigarh 2205,2780,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 75th percentile of PM10 in July 2020?,Assam 2206,2781,spatial_aggregation,Which station has the 3rd highest median PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest median PM10 in April 2020.,"Pallavpuram Phase 2, Meerut - UPPCB" 2207,2782,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 25th percentile of PM2.5 value in September 2022.,Aizawl 2208,2784,spatial_aggregation,Which station has the highest average PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM2.5 in October 2022.,"Burari Crossing, Delhi - IMD" 2209,2785,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM2.5 in October 2023?,"Sector 11, Faridabad - HSPCB" 2210,2786,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest 25th percentile of PM2.5 in May 2024.,Sri Ganganagar 2211,2787,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for September 2021.,"Lajpat Nagar, Moradabad - UPPCB" 2212,2789,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest 25th percentile of PM2.5 in June 2023.,Bidar 2213,2790,spatial_aggregation,Which station has the highest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM2.5 in October 2018?,"CRRI Mathura Road, Delhi - IMD" 2214,2791,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM2.5 in November 2021.,Puducherry 2215,2792,spatial_aggregation,Which city has the 3rd lowest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest average PM10 value in July 2019.,Eloor 2216,2794,spatial_aggregation,Which station has the lowest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest median PM2.5 in December 2021.,"Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 2217,2796,spatial_aggregation,Which city has the highest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest 25th percentile of PM10 in September 2022.,Tirupur 2218,2797,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 75th percentile of PM10 for September 2018.,Jalandhar 2219,2799,spatial_aggregation,Which state has the highest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 25th percentile of PM10 in September 2022.,Himachal Pradesh 2220,2801,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 75th percentile of PM2.5 in June 2024.,Delhi 2221,2802,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 75th percentile of PM10 value in December 2021.,"Sector - 62, Noida - IMD" 2222,2803,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest average PM2.5 during November 2022?,Sikkim 2223,2804,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest 25th percentile of PM10 in March 2023.,Munger 2224,2805,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in August 2021?,"Kadri, Mangalore - KSPCB" 2225,2807,spatial_aggregation,Which station has the 3rd highest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 for August 2019.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 2226,2808,spatial_aggregation,Which station has the 3rd highest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average PM2.5 in November 2024?,"Bawana, Delhi - DPCC" 2227,2809,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest median PM2.5 in October 2024.,Gangtok 2228,2811,spatial_aggregation,Which station has the 3rd highest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM2.5 in March 2022.,"Mazgaon, Mumbai - IITM" 2229,2813,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 25th percentile of PM10 during February 2020?,"Urban, Chamarajanagar - KSPCB" 2230,2814,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM10 in April 2018.,Haryana 2231,2815,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM2.5 in June 2019?,Chandrapur 2232,2816,spatial_aggregation,Which state has the highest median PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest median PM10 in November 2019.,Uttar Pradesh 2233,2817,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest median PM2.5 for March 2021.,Chandigarh 2234,2819,spatial_aggregation,Which state has the lowest median PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM10 in October 2018.,Odisha 2235,2821,spatial_aggregation,Which city has the 2nd lowest average PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest average PM10 in January 2024.,Gangtok 2236,2822,spatial_aggregation,Which state has the 3rd lowest median PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest median PM10 value in May 2020.,Tamil Nadu 2237,2824,spatial_aggregation,Which city has the highest median PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest median PM2.5 in August 2022.,Pune 2238,2825,spatial_aggregation,Which state has the highest median PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in October 2021?,Delhi 2239,2826,spatial_aggregation,Which state has the 2nd highest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest median PM2.5 in June 2023.,Tripura 2240,2828,spatial_aggregation,Which station has the 2nd highest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median PM10 in December 2024?,"Anand Vihar, Delhi - DPCC" 2241,2830,spatial_aggregation,Which state has the 2nd highest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM10 in October 2024?,Himachal Pradesh 2242,2831,spatial_aggregation,Which state has the 3rd highest average PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest average PM2.5 in October 2021.,Uttar Pradesh 2243,2832,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest 25th percentile of PM2.5 value in September 2019.,Sirsa 2244,2833,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd lowest 75th percentile of PM10 during November 2024?,Aizawl 2245,2834,spatial_aggregation,Which city has the highest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest average PM2.5 in February 2024.,Byrnihat 2246,2835,spatial_aggregation,Which state has the lowest average PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest average PM10 in February 2019?,Kerala 2247,2836,spatial_aggregation,Which city has the 3rd lowest median PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest median PM10 in March 2020.,Amaravati 2248,2837,spatial_aggregation,Which station has the 2nd highest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM10 for July 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 2249,2838,spatial_aggregation,Which state has the 3rd highest median PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest median PM10 in December 2023?,Bihar 2250,2839,spatial_aggregation,Which state has the 2nd lowest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM10 in January 2023.,Mizoram 2251,2840,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 75th percentile of PM2.5 in January 2022?,Mizoram 2252,2841,spatial_aggregation,Which city has the 2nd highest median PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM2.5 in August 2023.,Kaithal 2253,2842,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 75th percentile of PM2.5 value in September 2020.,"Sanathnagar, Hyderabad - TSPCB" 2254,2843,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest average PM2.5 during July 2024?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2255,2844,spatial_aggregation,Which state has the 3rd highest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM2.5 in February 2024.,Assam 2256,2845,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 75th percentile of PM2.5 in October 2019?,Delhi 2257,2846,spatial_aggregation,Which city has the 3rd lowest median PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest median PM10 in June 2020.,Pune 2258,2847,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile of PM2.5 for December 2020.,Mizoram 2259,2848,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM10 in December 2021?,Haryana 2260,2849,spatial_aggregation,Which city has the highest median PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM2.5 in February 2023.,Begusarai 2261,2850,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM10 in November 2024?,Tirunelveli 2262,2851,spatial_aggregation,Which state has the 2nd highest average PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM2.5 in April 2018.,Delhi 2263,2852,spatial_aggregation,Which state has the 2nd highest median PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest median PM10 value in July 2024.,Delhi 2264,2853,spatial_aggregation,Which station has the lowest average PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest average PM2.5 during December 2018?,"Bandhavgar Colony, Satna - Birla Cement" 2265,2854,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest median PM2.5 in August 2019.,Bathinda 2266,2855,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM10 in October 2022?,Mizoram 2267,2856,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station that had the 2nd lowest median PM2.5 in January 2018.,"MIDC Khutala, Chandrapur - MPCB" 2268,2857,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 75th percentile of PM10 for November 2023.,Bihar 2269,2858,spatial_aggregation,Which station has the 3rd highest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest median PM2.5 in October 2018?,"Narela, Delhi - DPCC" 2270,2859,spatial_aggregation,Which state has the 3rd lowest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest median PM10 in November 2018.,Karnataka 2271,2860,spatial_aggregation,Which state has the 2nd lowest median PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest median PM10 in March 2023?,Sikkim 2272,2861,spatial_aggregation,Which state has the lowest median PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in March 2021.,Meghalaya 2273,2863,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 75th percentile of PM10 during April 2024?,"Science Center, Surat - SMC" 2274,2864,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 25th percentile of PM2.5 in January 2023.,Delhi 2275,2866,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 25th percentile of PM10 in December 2020.,"Sanegurava Halli, Bengaluru - KSPCB" 2276,2867,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM10 for April 2018.,Amaravati 2277,2868,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest average PM2.5 in October 2020?,Meghalaya 2278,2869,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 75th percentile of PM2.5 in October 2021.,Meghalaya 2279,2871,spatial_aggregation,Which city has the lowest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest median PM10 in June 2022.,Udupi 2280,2873,spatial_aggregation,Which station has the lowest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest median PM2.5 during July 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2281,2874,spatial_aggregation,Which city has the 2nd lowest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest median PM10 in August 2024.,Gangtok 2282,2876,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest 25th percentile of PM10 in September 2024.,"Wazirpur, Delhi - DPCC" 2283,2877,spatial_aggregation,Which station has the highest 25th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM10 for April 2024.,"Muradpur, Patna - BSPCB" 2284,2878,spatial_aggregation,Which state has the 3rd lowest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM10 in December 2024?,Karnataka 2285,2879,spatial_aggregation,Which city has the highest 75th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest 75th percentile of PM10 in August 2023.,Chengalpattu 2286,2880,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 75th percentile of PM10 in February 2021?,Gadag 2287,2881,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM2.5 in November 2022.,"Tamaka Ind. Area, Kolar - KSPCB" 2288,2882,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 75th percentile of PM2.5 value in December 2021.,Meghalaya 2289,2883,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 25th percentile of PM2.5 during February 2022?,Aizawl 2290,2885,spatial_aggregation,Which station has the 2nd lowest median PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM10 in March 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 2291,2886,spatial_aggregation,Which station has the highest 25th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 25th percentile of PM10 in September 2019.,"Sirifort, Delhi - CPCB" 2292,2887,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest 75th percentile of PM2.5 for November 2018.,Ghaziabad 2293,2888,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest 25th percentile of PM2.5 in May 2023?,"Sardar Patel Nagar, Dhanbad - JSPCB" 2294,2889,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest 25th percentile of PM10 in October 2021.,Madikeri 2295,2890,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM2.5 in February 2024?,Varanasi 2296,2892,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest 75th percentile of PM10 value in June 2021.,Arunachal Pradesh 2297,2893,spatial_aggregation,Which state has the lowest average PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest average PM10 during December 2018?,Kerala 2298,2894,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest 75th percentile of PM10 in September 2022.,Bhilai 2299,2895,spatial_aggregation,Which state has the 2nd highest median PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM10 in September 2019?,Uttar Pradesh 2300,2896,spatial_aggregation,Which station has the highest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest median PM2.5 in August 2020.,"GIDC, Nandesari - Nandesari Ind. Association" 2301,2898,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM2.5 in January 2019?,Patiala 2302,2900,spatial_aggregation,Which station has the 2nd highest average PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM2.5 in June 2018?,"ITO, Delhi - CPCB" 2303,2901,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM2.5 in January 2021.,Noida 2304,2902,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 75th percentile of PM2.5 value in May 2022.,Gangtok 2305,2904,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 75th percentile of PM10 in December 2021.,Nagaland 2306,2905,spatial_aggregation,Which city has the 3rd lowest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in March 2019?,Vijayawada 2307,2906,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest median PM2.5 in May 2023.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 2308,2907,spatial_aggregation,Which city has the 3rd highest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM2.5 for November 2024.,Hajipur 2309,2908,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM2.5 in June 2019?,Uttar Pradesh 2310,2909,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 25th percentile of PM10 in January 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 2311,2910,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in February 2021?,Shillong 2312,2911,spatial_aggregation,Which station has the 2nd lowest average PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM10 in May 2019.,"Manali Village, Chennai - TNPCB" 2313,2912,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 75th percentile of PM2.5 value in April 2018.,Andhra Pradesh 2314,2913,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest 75th percentile of PM10 during October 2021?,Puducherry 2315,2915,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM2.5 in June 2018?,Tirupati 2316,2916,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest 25th percentile of PM2.5 in December 2022.,Himachal Pradesh 2317,2917,spatial_aggregation,Which city has the lowest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest average PM2.5 for November 2018.,Satna 2318,2918,spatial_aggregation,Which state has the 2nd highest median PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM10 in February 2024?,Delhi 2319,2919,spatial_aggregation,Which city has the 3rd highest median PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest median PM10 in December 2019.,Greater Noida 2320,2920,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM10 in August 2019?,"Sirifort, Delhi - CPCB" 2321,2922,spatial_aggregation,Which state has the 2nd highest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest average PM2.5 value in November 2018.,Uttar Pradesh 2322,2924,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM2.5 in May 2018.,"Collectorate, Jodhpur - RSPCB" 2323,2925,spatial_aggregation,Which state has the lowest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest average PM2.5 in September 2020?,Mizoram 2324,2927,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM10 for April 2024.,Jharkhand 2325,2928,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM10 in April 2024?,"Anand Vihar, Delhi - DPCC" 2326,2931,spatial_aggregation,Which station has the highest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM2.5 in June 2023.,"Muradpur, Patna - BSPCB" 2327,2932,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest 25th percentile of PM10 value in February 2020.,Uttar Pradesh 2328,2933,spatial_aggregation,Which city has the highest median PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest median PM10 during March 2019?,Ballabgarh 2329,2934,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 75th percentile of PM10 in July 2023.,"LGBI Airport, Guwahati - PCBA" 2330,2935,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 25th percentile of PM2.5 in March 2022?,Nagpur 2331,2936,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station that had the 2nd lowest average PM2.5 in April 2019.,"PWD Grounds, Vijayawada - APPCB" 2332,2937,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 25th percentile of PM2.5 for July 2023.,"Tarapur, Silchar - PCBA" 2333,2939,spatial_aggregation,Which station has the highest 75th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 75th percentile of PM10 in December 2020.,"Loni, Ghaziabad - UPPCB" 2334,2941,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM2.5 in December 2022.,Madikeri 2335,2942,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 75th percentile of PM2.5 value in March 2019.,Varanasi 2336,2945,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM2.5 in April 2023?,"Muradpur, Patna - BSPCB" 2337,2948,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 75th percentile of PM2.5 in December 2018?,Noida 2338,2949,spatial_aggregation,Which city has the lowest median PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest median PM10 in March 2024.,Tumakuru 2339,2950,spatial_aggregation,Which state has the 2nd highest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM10 in February 2023?,Delhi 2340,2951,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 25th percentile of PM10 in May 2024.,Himachal Pradesh 2341,2953,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest 75th percentile of PM2.5 during May 2018?,Bhiwadi 2342,2954,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest 75th percentile of PM10 in February 2018.,Bhiwadi 2343,2956,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest average PM2.5 in October 2018.,Thiruvananthapuram 2344,2957,spatial_aggregation,Which city has the lowest median PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM10 for August 2018.,Kolar 2345,2958,spatial_aggregation,Which state has the highest average PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM2.5 in January 2020?,Delhi 2346,2959,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest 25th percentile of PM10 in August 2024.,Gangtok 2347,2960,spatial_aggregation,Which city has the 2nd highest average PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM2.5 in July 2020?,Ballabgarh 2348,2961,spatial_aggregation,Which station has the 2nd highest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM2.5 in June 2023.,"Sector-51, Gurugram - HSPCB" 2349,2962,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 75th percentile of PM10 value in August 2022.,"Zero Point GICI, Gangtok - SSPCB" 2350,2963,spatial_aggregation,Which state has the highest average PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest average PM2.5 during April 2021?,Delhi 2351,2964,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM2.5 in May 2018.,Rajasthan 2352,2966,spatial_aggregation,Which state has the highest 25th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest 25th percentile of PM10 in April 2019.,Uttar Pradesh 2353,2967,spatial_aggregation,Which station has the 3rd lowest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest median PM10 for September 2021.,"Kadri, Mangalore - KSPCB" 2354,2968,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 75th percentile of PM10 in May 2019?,Kerala 2355,2969,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest 25th percentile of PM10 in December 2023.,Aizawl 2356,2970,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM2.5 in June 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2357,2971,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in March 2024.,"Maldahiya, Varanasi - UPPCB" 2358,2972,spatial_aggregation,Which station has the lowest median PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest median PM10 value in April 2021.,"Brahmagiri, Udupi - KSPCB" 2359,2974,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 75th percentile of PM10 in March 2023.,Damoh 2360,2975,spatial_aggregation,Which city has the lowest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM2.5 in June 2023?,Gangtok 2361,2976,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest 75th percentile of PM10 in January 2023.,Katihar 2362,2977,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 75th percentile of PM2.5 for November 2024.,Chandigarh 2363,2978,spatial_aggregation,Which station has the 3rd highest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest median PM2.5 in January 2019?,"Rabindra Bharati University, Kolkata - WBPCB" 2364,2979,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest 25th percentile of PM10 in February 2019.,Delhi 2365,2980,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM2.5 in March 2024?,Maihar 2366,2981,spatial_aggregation,Which station has the 3rd lowest average PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest average PM10 in December 2019.,"Urban, Chamarajanagar - KSPCB" 2367,2982,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest average PM2.5 value in July 2020.,Meghalaya 2368,2983,spatial_aggregation,Which state has the lowest average PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest average PM2.5 during January 2021?,Meghalaya 2369,2984,spatial_aggregation,Which state has the 3rd highest average PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM2.5 in September 2024.,Assam 2370,2985,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 25th percentile of PM10 in September 2022?,Gangtok 2371,2986,spatial_aggregation,Which station has the 3rd highest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station that had the 3rd highest average PM2.5 in January 2023.,"Kamalnath Nagar, Bettiah - BSPCB" 2372,2987,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile of PM10 for February 2024.,Manipur 2373,2989,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest 25th percentile of PM2.5 in June 2020.,"Talkatora District Industries Center, Lucknow - CPCB" 2374,2990,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM2.5 in November 2023?,Sikkim 2375,2991,spatial_aggregation,Which state has the highest 25th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 25th percentile of PM10 in September 2023.,Himachal Pradesh 2376,2992,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 75th percentile of PM2.5 value in November 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2377,2994,spatial_aggregation,Which station has the highest average PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM2.5 in August 2021.,"GIDC, Nandesari - Nandesari Ind. Association" 2378,2995,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM2.5 in October 2018?,Nagpur 2379,2996,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM10 in June 2021.,"Brahmagiri, Udupi - KSPCB" 2380,2997,spatial_aggregation,Which station has the highest median PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM10 for November 2022.,"Kamalnath Nagar, Bettiah - BSPCB" 2381,2998,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 75th percentile of PM2.5 in January 2018?,Uttar Pradesh 2382,2999,spatial_aggregation,Which station has the 3rd lowest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest average PM10 in May 2020.,"Borivali East, Mumbai - MPCB" 2383,3000,spatial_aggregation,Which state has the highest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 25th percentile of PM10 in March 2022?,Delhi 2384,3001,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 25th percentile of PM2.5 in February 2021.,Rupnagar 2385,3002,spatial_aggregation,Which station has the 3rd highest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest median PM10 value in June 2019.,"Loni, Ghaziabad - UPPCB" 2386,3003,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM10 during September 2022?,Haryana 2387,3004,spatial_aggregation,Which state has the lowest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM2.5 in July 2022.,Mizoram 2388,3005,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM10 in January 2021?,Karnataka 2389,3006,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 25th percentile of PM2.5 in June 2022.,Manipur 2390,3008,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM2.5 in January 2021?,Bagalkot 2391,3009,spatial_aggregation,Which state has the highest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile of PM10 in April 2022.,Delhi 2392,3010,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM2.5 in June 2020?,"Borivali East, Mumbai - MPCB" 2393,3011,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 25th percentile of PM2.5 in December 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2394,3012,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest 75th percentile of PM10 value in July 2021.,"Chandni Chowk, Delhi - IITM" 2395,3013,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 75th percentile of PM10 during August 2019?,Eloor 2396,3014,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 75th percentile of PM10 in April 2021.,Delhi 2397,3015,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM10 in November 2021?,"D M Colony, Bihar Sharif - BSPCB" 2398,3016,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest 25th percentile of PM10 in July 2021.,Sonipat 2399,3017,spatial_aggregation,Which state has the lowest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM2.5 for December 2022.,Mizoram 2400,3018,spatial_aggregation,Which state has the highest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average PM2.5 in April 2019?,Uttar Pradesh 2401,3019,spatial_aggregation,Which state has the 2nd lowest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM10 in February 2021.,Puducherry 2402,3021,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM2.5 in November 2023.,Dholpur 2403,3023,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest median PM2.5 during September 2021?,"Diwator Nagar, Koppal - KSPCB" 2404,3024,spatial_aggregation,Which state has the 3rd highest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM10 in April 2019.,Haryana 2405,3025,spatial_aggregation,Which state has the lowest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest average PM2.5 in December 2022?,Mizoram 2406,3026,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest 75th percentile of PM10 in February 2019.,Delhi 2407,3027,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 75th percentile of PM2.5 for October 2023.,Mizoram 2408,3028,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile of PM2.5 in October 2023?,Delhi 2409,3029,spatial_aggregation,Which state has the 2nd highest median PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM10 in October 2023.,Haryana 2410,3030,spatial_aggregation,Which station has the highest 25th percentile of PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM10 in October 2023?,"Mundka, Delhi - DPCC" 2411,3032,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM2.5 value in September 2019.,Bhiwadi 2412,3034,spatial_aggregation,Which city has the 2nd highest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM10 in November 2024.,Sonipat 2413,3035,spatial_aggregation,Which city has the lowest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest average PM2.5 in February 2023?,Maihar 2414,3036,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest 75th percentile of PM2.5 in July 2022.,Saharsa 2415,3037,spatial_aggregation,Which station has the highest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM10 for April 2018.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 2416,3038,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest average PM2.5 in March 2022?,Chhattisgarh 2417,3039,spatial_aggregation,Which station has the highest average PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest average PM10 in February 2022.,"Anand Vihar, Delhi - DPCC" 2418,3041,spatial_aggregation,Which state has the lowest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM10 in November 2023.,Sikkim 2419,3042,spatial_aggregation,Which station has the lowest median PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest median PM2.5 value in October 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2420,3043,spatial_aggregation,Which state has the highest 75th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest 75th percentile of PM10 during March 2023?,Bihar 2421,3045,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM2.5 in May 2020?,Eloor 2422,3047,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM2.5 for July 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2423,3048,spatial_aggregation,Which station has the 3rd lowest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest average PM10 in April 2023?,"Shrivastav Colony, Damoh - MPPCB" 2424,3049,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 25th percentile of PM10 in March 2023.,"Brahmagiri, Udupi - KSPCB" 2425,3050,spatial_aggregation,Which city has the highest median PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in January 2021?,Faridabad 2426,3051,spatial_aggregation,Which station has the 2nd lowest average PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM10 in March 2023.,"Semmandalam, Cuddalore - TNPCB" 2427,3052,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest 25th percentile of PM10 value in September 2024.,Maihar 2428,3053,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest 25th percentile of PM2.5 during February 2020?,Delhi 2429,3054,spatial_aggregation,Which station has the highest 75th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 75th percentile of PM10 in September 2019.,"Sirifort, Delhi - CPCB" 2430,3055,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM10 in January 2019?,"Tamaka Ind. Area, Kolar - KSPCB" 2431,3056,spatial_aggregation,Which station has the highest average PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest average PM2.5 in October 2021.,"Loni, Ghaziabad - UPPCB" 2432,3057,spatial_aggregation,Which state has the highest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM10 for March 2021.,Assam 2433,3058,spatial_aggregation,Which state has the 3rd highest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest average PM10 in July 2022?,Delhi 2434,3059,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 75th percentile of PM10 in October 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2435,3061,spatial_aggregation,Which station has the 3rd highest average PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM2.5 in April 2023.,"Sector-19A Nerul, Navi Mumbai - IITM" 2436,3062,spatial_aggregation,Which state has the 3rd lowest average PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest average PM10 value in October 2022.,Arunachal Pradesh 2437,3063,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 25th percentile of PM10 during April 2022?,Sikkim 2438,3064,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest 25th percentile of PM2.5 in April 2023.,Surat 2439,3065,spatial_aggregation,Which station has the lowest median PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest median PM10 in May 2023?,"GIDC, Nandesari - Nandesari Ind. Association" 2440,3066,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest 75th percentile of PM10 in May 2022.,Gangtok 2441,3067,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 75th percentile of PM2.5 for July 2020.,"IHBAS, Dilshad Garden, Delhi - CPCB" 2442,3068,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM10 in October 2024?,Meghalaya 2443,3069,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest 75th percentile of PM10 in October 2018.,Delhi 2444,3071,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM2.5 in December 2024.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 2445,3072,spatial_aggregation,Which city has the 2nd highest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest average PM10 value in November 2020.,Greater Noida 2446,3073,spatial_aggregation,Which station has the 3rd highest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest median PM10 during September 2024?,"Mundka, Delhi - DPCC" 2447,3075,spatial_aggregation,Which city has the 2nd highest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM2.5 in July 2021?,Bhiwadi 2448,3077,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM2.5 for May 2022.,Vijayapura 2449,3079,spatial_aggregation,Which station has the 3rd highest median PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest median PM2.5 in March 2023.,"Muradpur, Patna - BSPCB" 2450,3080,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM10 in August 2022?,Udupi 2451,3081,spatial_aggregation,Which city has the highest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest median PM2.5 in June 2023.,Bidar 2452,3082,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 25th percentile of PM10 value in April 2022.,Haryana 2453,3083,spatial_aggregation,Which station has the 3rd highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest median PM10 during July 2022?,"Govt. High School Shikarpur, Patna - BSPCB" 2454,3084,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in March 2019.,Uttar Pradesh 2455,3085,spatial_aggregation,Which station has the highest average PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest average PM2.5 in September 2022?,"GM Office, Brajrajnagar - OSPCB" 2456,3087,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM2.5 for June 2018.,"Jayanagar 5th Block, Bengaluru - KSPCB" 2457,3088,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM10 in October 2018?,"Rohini, Delhi - DPCC" 2458,3089,spatial_aggregation,Which state has the lowest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest median PM10 in December 2024.,Meghalaya 2459,3090,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 75th percentile of PM10 in February 2024?,Araria 2460,3092,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest 75th percentile of PM10 value in May 2020.,"Loni, Ghaziabad - UPPCB" 2461,3095,spatial_aggregation,Which city has the highest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest average PM10 in March 2024?,Byrnihat 2462,3096,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM2.5 in March 2018.,Andhra Pradesh 2463,3098,spatial_aggregation,Which city has the lowest average PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM10 in November 2023?,Gangtok 2464,3099,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM2.5 in August 2022.,Sikkim 2465,3100,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 25th percentile of PM2.5 in November 2019?,Vijayawada 2466,3101,spatial_aggregation,Which state has the highest median PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM10 in November 2022.,Delhi 2467,3102,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 75th percentile of PM2.5 value in July 2020.,"Mini Secretariat, Charkhi Dadri - HSPCB" 2468,3103,spatial_aggregation,Which station has the 3rd lowest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest average PM10 during February 2020?,"SIDCO Kurichi, Coimbatore - TNPCB" 2469,3104,spatial_aggregation,Which city has the lowest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest average PM2.5 in December 2022.,Aizawl 2470,3106,spatial_aggregation,Which city has the 2nd highest median PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest median PM2.5 in May 2022.,Rohtak 2471,3107,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 25th percentile of PM2.5 for May 2020.,"Sector 11, Faridabad - HSPCB" 2472,3108,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM2.5 in February 2024?,Sivasagar 2473,3109,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile of PM2.5 in May 2022.,Haryana 2474,3110,spatial_aggregation,Which state has the 3rd lowest average PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest average PM10 in September 2021?,Odisha 2475,3111,spatial_aggregation,Which state has the highest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM10 in May 2019.,Uttar Pradesh 2476,3113,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 25th percentile of PM2.5 during January 2019?,"Punjab Agricultural University, Ludhiana - PPCB" 2477,3114,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM10 in July 2022.,Saharsa 2478,3115,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM2.5 in August 2022?,"DM College of Science, Imphal - Manipur PCB" 2479,3116,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest 25th percentile of PM2.5 in April 2022.,Haryana 2480,3117,spatial_aggregation,Which city has the 2nd highest median PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM10 for March 2023.,Begusarai 2481,3118,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 75th percentile of PM2.5 in December 2018?,Delhi 2482,3119,spatial_aggregation,Which city has the 2nd lowest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest median PM10 in May 2019.,Kolar 2483,3120,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 25th percentile of PM10 in January 2020?,Meghalaya 2484,3121,spatial_aggregation,Which city has the 3rd lowest average PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM10 in January 2018.,Tirupati 2485,3122,spatial_aggregation,Which station has the highest median PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest median PM2.5 value in September 2018.,"Tamaka Ind. Area, Kolar - KSPCB" 2486,3123,spatial_aggregation,Which state has the 3rd lowest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest median PM10 during October 2024?,Puducherry 2487,3124,spatial_aggregation,Which state has the highest median PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest median PM10 in May 2023.,Delhi 2488,3125,spatial_aggregation,Which city has the highest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM2.5 in August 2019?,Hubballi 2489,3127,spatial_aggregation,Which city has the 2nd highest average PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM2.5 for June 2022.,Rohtak 2490,3128,spatial_aggregation,Which city has the highest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile of PM10 in April 2020?,Kalaburagi 2491,3129,spatial_aggregation,Which city has the highest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest 75th percentile of PM10 in November 2021.,Bihar Sharif 2492,3130,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM2.5 in November 2018?,"MIDC Khutala, Chandrapur - MPCB" 2493,3131,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in September 2023.,Delhi 2494,3132,spatial_aggregation,Which state has the 2nd highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest median PM2.5 value in December 2021.,Bihar 2495,3133,spatial_aggregation,Which state has the 2nd highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM2.5 during March 2020?,Jharkhand 2496,3134,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest 75th percentile of PM2.5 in March 2020.,Andhra Pradesh 2497,3136,spatial_aggregation,Which station has the highest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest median PM10 in May 2024.,"Shadipur, Delhi - CPCB" 2498,3138,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM10 in December 2018?,Andhra Pradesh 2499,3139,spatial_aggregation,Which station has the 2nd lowest average PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest average PM10 in May 2022.,"Kompally Municipal Office, Hyderabad - TSPCB" 2500,3140,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in December 2020?,Ghaziabad 2501,3141,spatial_aggregation,Which state has the highest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM10 in September 2018.,Haryana 2502,3143,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM10 during February 2021?,Assam 2503,3144,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 25th percentile of PM2.5 in November 2018.,"Wazirpur, Delhi - DPCC" 2504,3145,spatial_aggregation,Which station has the highest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM10 in October 2020?,"Knowledge Park - V, Greater Noida - UPPCB" 2505,3147,spatial_aggregation,Which state has the 3rd highest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest average PM2.5 for September 2020.,Haryana 2506,3148,spatial_aggregation,Which state has the highest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest median PM2.5 in March 2022?,Assam 2507,3149,spatial_aggregation,Which city has the 2nd highest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest average PM10 in July 2023.,Sri Ganganagar 2508,3150,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM2.5 in October 2020?,"Talkatora District Industries Center, Lucknow - CPCB" 2509,3151,spatial_aggregation,Which station has the lowest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest average PM10 in February 2018.,"Sanegurava Halli, Bengaluru - KSPCB" 2510,3152,spatial_aggregation,Which city has the 2nd highest median PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest median PM10 value in April 2024.,Sri Ganganagar 2511,3153,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd lowest 25th percentile of PM10 during October 2020?,Madikeri 2512,3155,spatial_aggregation,Which city has the 3rd highest average PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in January 2022?,Munger 2513,3156,spatial_aggregation,Which state has the 3rd lowest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM10 in September 2018.,Maharashtra 2514,3157,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM10 for March 2021.,Jammu and Kashmir 2515,3158,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 75th percentile of PM2.5 in March 2023?,Tripura 2516,3159,spatial_aggregation,Which city has the 3rd lowest median PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest median PM10 in June 2021.,Koppal 2517,3160,spatial_aggregation,Which city has the 2nd lowest median PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM10 in June 2018?,Chandrapur 2518,3163,spatial_aggregation,Which station has the highest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest median PM10 during September 2018?,"Tamaka Ind. Area, Kolar - KSPCB" 2519,3167,spatial_aggregation,Which city has the 3rd highest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM2.5 for February 2022.,Saharsa 2520,3168,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM2.5 in March 2019?,"Hardev Nagar, Bathinda - PPCB" 2521,3169,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 75th percentile of PM2.5 in October 2018.,Gurugram 2522,3170,spatial_aggregation,Which station has the lowest average PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in February 2019?,"Bandhavgar Colony, Satna - Birla Cement" 2523,3172,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest 25th percentile of PM2.5 value in January 2023.,Sagar 2524,3173,spatial_aggregation,Which state has the lowest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest median PM2.5 during September 2020?,Mizoram 2525,3174,spatial_aggregation,Which station has the lowest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest median PM2.5 in August 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2526,3175,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM10 in July 2021?,Shillong 2527,3176,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 25th percentile of PM10 in June 2020.,"Hebbal 1st Stage, Mysuru - KSPCB" 2528,3177,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 25th percentile of PM2.5 for January 2019.,"Rohini, Delhi - DPCC" 2529,3179,spatial_aggregation,Which state has the 3rd highest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest average PM10 in September 2018.,Delhi 2530,3180,spatial_aggregation,Which state has the highest median PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in December 2024?,Tripura 2531,3181,spatial_aggregation,Which station has the 3rd highest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest median PM2.5 in March 2022.,"Mirchaibari, Katihar - BSPCB" 2532,3182,spatial_aggregation,Which city has the 2nd lowest median PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest median PM10 value in July 2021.,Udupi 2533,3183,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest average PM2.5 during August 2021?,"Diwator Nagar, Koppal - KSPCB" 2534,3184,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest median PM2.5 in March 2019.,Bathinda 2535,3185,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM2.5 in May 2018?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 2536,3186,spatial_aggregation,Which state has the 2nd highest average PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest average PM10 in March 2023.,Delhi 2537,3187,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 75th percentile of PM2.5 for June 2021.,Koppal 2538,3189,spatial_aggregation,Which state has the highest 25th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 25th percentile of PM10 in May 2018.,Uttar Pradesh 2539,3190,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM10 in July 2018?,Uttar Pradesh 2540,3191,spatial_aggregation,Which station has the highest average PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest average PM2.5 in November 2019.,"Loni, Ghaziabad - UPPCB" 2541,3193,spatial_aggregation,Which station has the 3rd lowest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest median PM10 during April 2019?,"Ratanpura, Rupnagar - Ambuja Cements" 2542,3194,spatial_aggregation,Which station has the 2nd lowest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest median PM10 in November 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 2543,3195,spatial_aggregation,Which state has the highest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in November 2021?,Delhi 2544,3196,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest 25th percentile of PM10 in September 2022.,Rajasthan 2545,3197,spatial_aggregation,Which state has the 2nd highest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest median PM10 for May 2024.,Himachal Pradesh 2546,3198,spatial_aggregation,Which city has the 2nd highest average PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM2.5 in June 2023?,Bidar 2547,3199,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 75th percentile of PM10 in November 2018.,Ghaziabad 2548,3200,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM10 in February 2021?,"New Industrial Town, Faridabad - HSPCB" 2549,3201,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM2.5 in November 2019.,Karnataka 2550,3203,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 25th percentile of PM2.5 during June 2018?,"Bandhavgar Colony, Satna - Birla Cement" 2551,3204,spatial_aggregation,Which city has the highest 75th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 in December 2020.,Lucknow 2552,3205,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM2.5 in December 2021?,"Jahangirpuri, Delhi - DPCC" 2553,3206,spatial_aggregation,Which city has the 3rd lowest average PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest average PM10 in May 2019.,Kolar 2554,3207,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM10 for July 2021.,Udupi 2555,3208,spatial_aggregation,Which state has the 2nd highest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest average PM2.5 in July 2021?,Haryana 2556,3209,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest 25th percentile of PM2.5 in June 2020.,Uttar Pradesh 2557,3210,spatial_aggregation,Which station has the 2nd highest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest median PM10 in March 2022?,"Police Line, Saharsa - BSPCB" 2558,3212,spatial_aggregation,Which state has the 3rd highest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest median PM10 value in October 2020.,Haryana 2559,3213,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest median PM2.5 during March 2019?,"PWD Grounds, Vijayawada - APPCB" 2560,3214,spatial_aggregation,Which city has the highest 75th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 in January 2022.,Saharsa 2561,3215,spatial_aggregation,Which state has the 3rd highest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest median PM10 in May 2021?,Delhi 2562,3216,spatial_aggregation,Which station has the lowest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest median PM2.5 in January 2020.,"Bandhavgar Colony, Satna - Birla Cement" 2563,3217,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM10 for March 2020.,"Secretariat, Amaravati - APPCB" 2564,3218,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM10 in November 2019?,"Dwarka-Sector 8, Delhi - DPCC" 2565,3219,spatial_aggregation,Which city has the lowest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest average PM10 in February 2018.,Tirupati 2566,3220,spatial_aggregation,Which station has the lowest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM10 in May 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2567,3223,spatial_aggregation,Which station has the 3rd highest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest average PM2.5 during January 2019?,"Rohini, Delhi - DPCC" 2568,3224,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest median PM2.5 in June 2018.,Maharashtra 2569,3225,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 75th percentile of PM10 in December 2019?,Chamarajanagar 2570,3226,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM10 in December 2022.,"Brahmagiri, Udupi - KSPCB" 2571,3227,spatial_aggregation,Which city has the lowest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest average PM2.5 for July 2024.,Manguraha 2572,3228,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM10 in March 2024?,Tripura 2573,3230,spatial_aggregation,Which station has the 3rd lowest average PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest average PM10 in September 2019?,"Rabindra Sarobar, Kolkata - WBPCB" 2574,3232,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest 25th percentile of PM2.5 value in September 2024.,Punjab 2575,3233,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest 25th percentile of PM10 during June 2019?,Ballabgarh 2576,3235,spatial_aggregation,Which state has the highest average PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM10 in June 2024?,Delhi 2577,3237,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM2.5 for November 2020.,Delhi 2578,3238,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM2.5 in April 2019?,Palwal 2579,3239,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the 2nd highest 25th percentile of PM2.5 in February 2023.,"DRCC Anandpur, Begusarai - BSPCB" 2580,3240,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest average PM2.5 in January 2023?,"Tarapur, Silchar - PCBA" 2581,3241,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM10 in January 2022.,Saharsa 2582,3242,spatial_aggregation,Which city has the 3rd highest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest median PM2.5 value in January 2023.,Katihar 2583,3243,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest 25th percentile of PM10 during September 2019?,Kerala 2584,3244,spatial_aggregation,Which city has the 2nd highest median PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest median PM10 in December 2021.,Singrauli 2585,3245,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM10 in December 2022?,Arunachal Pradesh 2586,3246,spatial_aggregation,Which station has the highest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 25th percentile of PM10 in August 2018.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 2587,3247,spatial_aggregation,Which state has the lowest average PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest average PM2.5 for August 2023.,Sikkim 2588,3248,spatial_aggregation,Which state has the highest 25th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile of PM10 in October 2018?,Jharkhand 2589,3249,spatial_aggregation,Which state has the lowest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest average PM10 in April 2023.,Arunachal Pradesh 2590,3250,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 25th percentile of PM10 in May 2021?,Arunachal Pradesh 2591,3251,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM2.5 in December 2022.,Chhattisgarh 2592,3253,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest 25th percentile of PM2.5 during July 2018?,Andhra Pradesh 2593,3254,spatial_aggregation,Which state has the lowest average PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest average PM10 in September 2020.,Mizoram 2594,3255,spatial_aggregation,Which city has the highest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in September 2024?,Nandesari 2595,3256,spatial_aggregation,Which state has the 2nd highest median PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest median PM2.5 in April 2023.,Tripura 2596,3258,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM2.5 in April 2024?,Sikkim 2597,3259,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest 25th percentile of PM10 in July 2022.,Chhattisgarh 2598,3260,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 75th percentile of PM2.5 in July 2022?,"Police Line, Saharsa - BSPCB" 2599,3261,spatial_aggregation,Which city has the 3rd highest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM2.5 in July 2021.,Jodhpur 2600,3262,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest 75th percentile of PM10 value in February 2023.,"Brahmagiri, Udupi - KSPCB" 2601,3263,spatial_aggregation,Which city has the 3rd highest average PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest average PM10 during September 2022?,Ambala 2602,3264,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 25th percentile of PM10 in June 2019.,Kerala 2603,3265,spatial_aggregation,Which city has the 2nd lowest average PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest average PM10 in February 2022?,Shillong 2604,3266,spatial_aggregation,Which state has the 2nd highest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest median PM10 in April 2023.,Delhi 2605,3267,spatial_aggregation,Which state has the lowest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM2.5 for April 2018.,Andhra Pradesh 2606,3268,spatial_aggregation,Which city has the highest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest median PM2.5 in May 2019?,Jodhpur 2607,3269,spatial_aggregation,Which city has the highest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM10 in April 2019.,Ghaziabad 2608,3270,spatial_aggregation,Which city has the 3rd lowest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM10 in April 2023?,Damoh 2609,3271,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 25th percentile of PM10 in May 2018.,Bulandshahr 2610,3272,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 25th percentile of PM2.5 value in April 2019.,Rajamahendravaram 2611,3274,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 75th percentile of PM2.5 in November 2021.,Shillong 2612,3275,spatial_aggregation,Which state has the highest median PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM10 in September 2019?,Delhi 2613,3276,spatial_aggregation,Which city has the lowest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest median PM10 in June 2024.,Koppal 2614,3278,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM2.5 in December 2019?,Kerala 2615,3279,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city with the 2nd lowest median PM2.5 in April 2022.,Puducherry 2616,3280,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 25th percentile of PM2.5 in November 2022?,Haryana 2617,3281,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM2.5 in December 2019.,Karnataka 2618,3282,spatial_aggregation,Which city has the 3rd highest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest median PM2.5 value in June 2022.,Bhiwani 2619,3284,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 25th percentile of PM10 in January 2019.,Amritsar 2620,3285,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 25th percentile of PM2.5 in February 2022?,Delhi 2621,3287,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM2.5 for March 2023.,Gangtok 2622,3289,spatial_aggregation,Which state has the 2nd highest median PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM2.5 in April 2020.,Uttar Pradesh 2623,3290,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM2.5 in January 2024?,Jammu and Kashmir 2624,3292,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest 25th percentile of PM2.5 value in August 2024.,Delhi 2625,3294,spatial_aggregation,Which city has the 3rd highest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest median PM10 in November 2021.,Saharsa 2626,3295,spatial_aggregation,Which city has the highest median PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM2.5 in September 2021?,Nandesari 2627,3296,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest median PM2.5 in November 2021.,Aizawl 2628,3297,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM10 for September 2022.,Gangtok 2629,3298,spatial_aggregation,Which station has the 2nd highest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median PM2.5 in November 2021?,"Loni, Ghaziabad - UPPCB" 2630,3300,spatial_aggregation,Which state has the lowest median PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest median PM10 in June 2023?,Sikkim 2631,3302,spatial_aggregation,Which city has the 2nd lowest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest median PM10 value in September 2018.,Bulandshahr 2632,3305,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM2.5 in January 2023?,Madikeri 2633,3306,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest average PM2.5 in September 2020.,Kozhikode 2634,3307,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 25th percentile of PM10 for March 2018.,Varanasi 2635,3308,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 25th percentile of PM10 in April 2022?,Amaravati 2636,3309,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 25th percentile of PM10 in May 2019.,Tamil Nadu 2637,3311,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM2.5 in July 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2638,3312,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest 25th percentile of PM2.5 value in March 2019.,Varanasi 2639,3313,spatial_aggregation,Which station has the lowest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest median PM2.5 during May 2019?,"Udyogamandal, Eloor - Kerala PCB" 2640,3314,spatial_aggregation,Which city has the 3rd highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest median PM2.5 in July 2022.,Chandrapur 2641,3315,spatial_aggregation,Which city has the 3rd highest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in July 2022?,Samastipur 2642,3316,spatial_aggregation,Which state has the 3rd highest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest median PM2.5 in January 2018.,Delhi 2643,3317,spatial_aggregation,Which state has the 3rd highest average PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest average PM2.5 for April 2022.,Bihar 2644,3319,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 75th percentile of PM2.5 in March 2024.,"Bhelupur, Varanasi - UPPCB" 2645,3320,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM10 in April 2020?,"Sanegurava Halli, Bengaluru - KSPCB" 2646,3321,spatial_aggregation,Which state has the highest average PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM2.5 in March 2024.,Tripura 2647,3322,spatial_aggregation,Which city has the 3rd highest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the 3rd highest median PM2.5 value in June 2018.,Gurugram 2648,3325,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM10 in January 2022?,Shillong 2649,3326,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 75th percentile of PM2.5 in February 2019.,"Wazirpur, Delhi - DPCC" 2650,3327,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 for January 2024.,"Zero Point GICI, Gangtok - SSPCB" 2651,3328,spatial_aggregation,Which state has the 3rd highest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest average PM2.5 in September 2019?,Delhi 2652,3329,spatial_aggregation,Which station has the 3rd highest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest average PM10 in March 2024.,"Teri Gram, Gurugram - HSPCB" 2653,3330,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM10 in February 2024?,"Manipur University, Imphal - Manipur PCB" 2654,3332,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest average PM2.5 value in September 2024.,"Zero Point GICI, Gangtok - SSPCB" 2655,3333,spatial_aggregation,Which state has the 3rd highest median PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest median PM2.5 during June 2024?,Delhi 2656,3334,spatial_aggregation,Which state has the 3rd highest average PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest average PM10 in August 2023.,Rajasthan 2657,3335,spatial_aggregation,Which city has the highest median PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in November 2022?,Bettiah 2658,3336,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM10 in August 2023.,"Tarapur, Silchar - PCBA" 2659,3337,spatial_aggregation,Which city has the lowest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM10 for October 2024.,Maihar 2660,3338,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 25th percentile of PM2.5 in April 2021?,Koppal 2661,3339,spatial_aggregation,Which state has the 3rd highest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the 3rd highest median PM10 in June 2024.,Chandigarh 2662,3340,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 75th percentile of PM2.5 in April 2024?,Delhi 2663,3341,spatial_aggregation,Which state has the highest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM10 in May 2024.,Delhi 2664,3342,spatial_aggregation,Which station has the 3rd highest average PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest average PM2.5 value in August 2023.,"Sector-51, Gurugram - HSPCB" 2665,3343,spatial_aggregation,Which state has the 2nd lowest average PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest average PM10 during July 2021?,Mizoram 2666,3344,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest 25th percentile of PM2.5 in July 2018.,Thane 2667,3345,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM2.5 in November 2024?,Hajipur 2668,3347,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM10 for December 2020.,"New Industrial Town, Faridabad - HSPCB" 2669,3348,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM10 in July 2019?,Uttar Pradesh 2670,3349,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 75th percentile of PM2.5 in June 2022.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 2671,3350,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM2.5 in August 2021?,Aizawl 2672,3351,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in February 2023.,Begusarai 2673,3352,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 25th percentile of PM10 value in June 2024.,"Zero Point GICI, Gangtok - SSPCB" 2674,3353,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest average PM2.5 during April 2020?,Tamil Nadu 2675,3354,spatial_aggregation,Which state has the 3rd lowest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest average PM10 in May 2018.,Andhra Pradesh 2676,3355,spatial_aggregation,Which station has the 2nd highest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM2.5 in October 2018?,"Vikas Sadan, Gurugram - HSPCB" 2677,3356,spatial_aggregation,Which city has the 3rd lowest median PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city that had the 3rd lowest median PM10 in January 2020.,Mysuru 2678,3357,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 for June 2018.,Thane 2679,3358,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 25th percentile of PM2.5 in December 2023?,Nandesari 2680,3359,spatial_aggregation,Which city has the highest median PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest median PM2.5 in April 2021.,Bhiwadi 2681,3360,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM2.5 in June 2018?,"ITO, Delhi - CPCB" 2682,3361,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM2.5 in August 2023.,Nagaland 2683,3362,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest median PM2.5 value in December 2024.,Karnataka 2684,3363,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest average PM2.5 during November 2021?,Meghalaya 2685,3365,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM2.5 in January 2024?,Satna 2686,3366,spatial_aggregation,Which city has the 2nd lowest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest average PM10 in May 2020.,Shillong 2687,3367,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM2.5 for November 2018.,"Tamaka Ind. Area, Kolar - KSPCB" 2688,3368,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM2.5 in July 2019?,"Urban, Chamarajanagar - KSPCB" 2689,3369,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest 25th percentile of PM2.5 in September 2023.,Pali 2690,3370,spatial_aggregation,Which station has the 2nd highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest median PM10 in February 2023?,"Central Academy for SFS, Byrnihat - PCBA" 2691,3371,spatial_aggregation,Which state has the 3rd lowest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM10 in June 2024.,Arunachal Pradesh 2692,3372,spatial_aggregation,Which state has the 2nd highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest median PM10 value in July 2022.,Himachal Pradesh 2693,3373,spatial_aggregation,Which state has the highest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest median PM2.5 during February 2022?,Delhi 2694,3374,spatial_aggregation,Which station has the 2nd highest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM10 in February 2018.,"Dwarka-Sector 8, Delhi - DPCC" 2695,3375,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM10 in March 2020?,"Anand Vihar, Delhi - DPCC" 2696,3376,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest 25th percentile of PM2.5 in June 2022.,Rohtak 2697,3377,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM2.5 for September 2020.,Rajasthan 2698,3379,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest 75th percentile of PM2.5 in August 2021.,Mandi Gobindgarh 2699,3380,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM10 in February 2022?,"MIT-Daudpur Kothi, Muzaffarpur - BSPCB" 2700,3381,spatial_aggregation,Which state has the 3rd highest average PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest average PM2.5 in September 2022.,Delhi 2701,3382,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the 3rd highest 25th percentile of PM10 value in May 2021.,"Murthal, Sonipat - HSPCB" 2702,3383,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest median PM2.5 during January 2024?,Mandikhera 2703,3384,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest 75th percentile of PM2.5 in September 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 2704,3385,spatial_aggregation,Which city has the 3rd lowest average PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in February 2022?,Maihar 2705,3386,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city that had the 2nd lowest median PM2.5 in June 2022.,Aizawl 2706,3387,spatial_aggregation,Which station has the lowest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM2.5 for November 2018.,"Bandhavgar Colony, Satna - Birla Cement" 2707,3388,spatial_aggregation,Which city has the highest 75th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile of PM10 in February 2023?,Hanumangarh 2708,3389,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile of PM2.5 in December 2019.,Delhi 2709,3390,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest average PM2.5 in November 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 2710,3392,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 25th percentile of PM2.5 value in March 2023.,"Chakala-Andheri East, Mumbai - IITM" 2711,3393,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 25th percentile of PM10 during April 2023?,"Civic Center, Bhilai - Bhilai Steel Plant" 2712,3396,spatial_aggregation,Which city has the 3rd highest average PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest average PM2.5 in April 2018.,Jodhpur 2713,3397,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM10 for April 2022.,Udupi 2714,3398,spatial_aggregation,Which city has the 2nd highest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM10 in September 2023?,Surat 2715,3399,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 75th percentile of PM10 in March 2022.,"Brahmagiri, Udupi - KSPCB" 2716,3400,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM10 in February 2022?,Saharsa 2717,3401,spatial_aggregation,Which state has the lowest median PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM10 in July 2020.,Meghalaya 2718,3402,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 75th percentile of PM10 value in March 2019.,Delhi 2719,3403,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest 25th percentile of PM10 during December 2023?,Gangtok 2720,3404,spatial_aggregation,Which city has the highest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 in November 2022.,Bettiah 2721,3405,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM10 in July 2022?,"Police Line, Saharsa - BSPCB" 2722,3406,spatial_aggregation,Which station has the 3rd highest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station that had the 3rd highest median PM10 in November 2023.,"New Industrial Town, Faridabad - HSPCB" 2723,3407,spatial_aggregation,Which city has the highest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM2.5 for November 2022.,Motihari 2724,3409,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest 25th percentile of PM2.5 in February 2023.,Maihar 2725,3410,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest 75th percentile of PM2.5 in July 2022?,Mizoram 2726,3412,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 25th percentile of PM2.5 value in May 2019.,Eloor 2727,3413,spatial_aggregation,Which city has the highest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the highest average PM2.5 during December 2021?,Delhi 2728,3414,spatial_aggregation,Which city has the 2nd lowest average PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest average PM10 in October 2020.,Madikeri 2729,3415,spatial_aggregation,Which state has the 2nd highest median PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM2.5 in February 2021?,Uttar Pradesh 2730,3416,spatial_aggregation,Which station has the highest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest average PM10 in August 2022.,"Tata Stadium, Jorapokhar - JSPCB" 2731,3418,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM10 in March 2023?,Assam 2732,3419,spatial_aggregation,Which station has the 2nd highest median PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the 2nd highest median PM10 in December 2020.,"Loni, Ghaziabad - UPPCB" 2733,3420,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 25th percentile of PM2.5 in January 2021?,Ghaziabad 2734,3422,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest 75th percentile of PM10 value in January 2018.,"PWD Grounds, Vijayawada - APPCB" 2735,3423,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM2.5 during April 2024?,Tripura 2736,3424,spatial_aggregation,Which station has the 2nd highest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM2.5 in May 2021.,"Sector 11, Faridabad - HSPCB" 2737,3427,spatial_aggregation,Which state has the 2nd highest median PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest median PM2.5 for August 2021.,Haryana 2738,3428,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM10 in February 2018?,Haryana 2739,3429,spatial_aggregation,Which station has the 3rd lowest average PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest average PM10 in January 2022.,"Brahmagiri, Udupi - KSPCB" 2740,3430,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM2.5 in December 2018?,"Jahangirpuri, Delhi - DPCC" 2741,3432,spatial_aggregation,Which city has the 2nd highest average PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest average PM10 value in December 2022.,Darbhanga 2742,3433,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest 25th percentile of PM10 during November 2019?,Tamil Nadu 2743,3434,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM10 in May 2023.,"Zero Point GICI, Gangtok - SSPCB" 2744,3435,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM2.5 in February 2019?,"Hardev Nagar, Bathinda - PPCB" 2745,3436,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city that had the 2nd highest 75th percentile of PM10 in May 2018.,Jodhpur 2746,3437,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 75th percentile of PM2.5 for December 2021.,"Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 2747,3438,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM2.5 in January 2022?,Chamarajanagar 2748,3439,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 25th percentile of PM2.5 in April 2021.,Puducherry 2749,3441,spatial_aggregation,Which city has the 2nd lowest average PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest average PM10 in July 2024.,Koppal 2750,3442,spatial_aggregation,Which state has the 3rd highest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest median PM2.5 value in October 2018.,Gujarat 2751,3443,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd lowest 25th percentile of PM2.5 during June 2021?,Koppal 2752,3444,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 75th percentile of PM2.5 in October 2023.,Sikkim 2753,3446,spatial_aggregation,Which station has the highest 25th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 25th percentile of PM10 in December 2023.,"Jahangirpuri, Delhi - DPCC" 2754,3447,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM10 for July 2024.,"Diwator Nagar, Koppal - KSPCB" 2755,3448,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM2.5 in June 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2756,3449,spatial_aggregation,Which state has the 2nd highest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest average PM10 in July 2019.,Uttar Pradesh 2757,3450,spatial_aggregation,Which city has the 3rd highest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM2.5 in May 2023?,Charkhi Dadri 2758,3451,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 75th percentile of PM10 in September 2019.,Haryana 2759,3452,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 25th percentile of PM2.5 value in May 2018.,Andhra Pradesh 2760,3453,spatial_aggregation,Which station has the 2nd lowest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest median PM10 during February 2022?,"Sahilara, Maihar - KJS Cements" 2761,3454,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 25th percentile of PM2.5 in August 2018.,Telangana 2762,3455,spatial_aggregation,Which station has the 3rd highest median PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in November 2020?,"Indirapuram, Ghaziabad - UPPCB" 2763,3456,spatial_aggregation,Which city has the 3rd highest average PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city that had the 3rd highest average PM2.5 in April 2019.,Ballabgarh 2764,3457,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM10 for August 2021.,Shillong 2765,3458,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 25th percentile of PM2.5 in May 2021?,Davanagere 2766,3459,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest median PM2.5 in August 2019.,Kerala 2767,3460,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest average PM2.5 in December 2018?,Chandrapur 2768,3461,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 25th percentile of PM10 in July 2023.,Gangtok 2769,3462,spatial_aggregation,Which city has the lowest average PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest average PM2.5 value in June 2018.,Satna 2770,3463,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest average PM2.5 during December 2019?,Kerala 2771,3465,spatial_aggregation,Which city has the lowest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM2.5 in March 2020?,Eloor 2772,3466,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 25th percentile of PM2.5 in October 2018.,"Vikas Sadan, Gurugram - HSPCB" 2773,3469,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest 75th percentile of PM2.5 in May 2020.,Delhi 2774,3470,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM2.5 in February 2024?,"IESD Banaras Hindu University, Varanasi - UPPCB" 2775,3471,spatial_aggregation,Which state has the 3rd highest average PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest average PM10 in October 2023.,Uttar Pradesh 2776,3472,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest 75th percentile of PM2.5 value in October 2024.,"Zero Point GICI, Gangtok - SSPCB" 2777,3473,spatial_aggregation,Which station has the lowest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest median PM10 during June 2019?,"Udyogamandal, Eloor - Kerala PCB" 2778,3475,spatial_aggregation,Which state has the lowest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest median PM2.5 in December 2023?,Sikkim 2779,3477,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 for September 2024.,"Sahilara, Maihar - KJS Cements" 2780,3478,spatial_aggregation,Which station has the 2nd highest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest average PM10 in November 2020?,"Loni, Ghaziabad - UPPCB" 2781,3479,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the 2nd lowest 75th percentile of PM10 in May 2018.,Jharkhand 2782,3480,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM2.5 in July 2019?,"Urban, Chamarajanagar - KSPCB" 2783,3481,spatial_aggregation,Which city has the lowest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest median PM2.5 in December 2021.,Gummidipoondi 2784,3482,spatial_aggregation,Which station has the 2nd lowest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest median PM10 value in May 2021.,"Tamaka Ind. Area, Kolar - KSPCB" 2785,3483,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest average PM2.5 during January 2021?,"Devaraj Urs Badavane, Davanagere - KSPCB" 2786,3484,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest 25th percentile of PM2.5 in April 2022.,Manesar 2787,3485,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 25th percentile of PM2.5 in January 2020?,Talcher 2788,3486,spatial_aggregation,Which city has the highest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city that had the highest average PM2.5 in May 2019.,Jodhpur 2789,3488,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 75th percentile of PM10 in August 2021?,Uttar Pradesh 2790,3489,spatial_aggregation,Which station has the 3rd lowest average PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest average PM10 in July 2020.,"Hebbal 1st Stage, Mysuru - KSPCB" 2791,3490,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 25th percentile of PM2.5 in June 2020?,Shillong 2792,3491,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in November 2021.,Delhi 2793,3492,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 75th percentile of PM10 value in August 2023.,Surat 2794,3493,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd lowest median PM2.5 during December 2018?,Tamil Nadu 2795,3494,spatial_aggregation,Which station has the highest average PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM10 in June 2021.,"Murthal, Sonipat - HSPCB" 2796,3495,spatial_aggregation,Which station has the 3rd lowest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM10 in June 2024?,"Zoo Park, Hyderabad - TSPCB" 2797,3496,spatial_aggregation,Which state has the 3rd highest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest median PM10 in April 2023.,Jharkhand 2798,3497,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 25th percentile of PM10 for August 2024.,"Wazirpur, Delhi - DPCC" 2799,3498,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile of PM2.5 in November 2019?,Bihar 2800,3500,spatial_aggregation,Which city has the 3rd lowest average PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in November 2021?,Koppal 2801,3501,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM10 in March 2023.,Jharkhand 2802,3502,spatial_aggregation,Which city has the highest average PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest average PM2.5 value in November 2022.,Motihari 2803,3503,spatial_aggregation,Which state has the 2nd highest average PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd highest average PM10 during October 2024?,Himachal Pradesh 2804,3506,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest average PM2.5 in May 2024.,"IESD Banaras Hindu University, Varanasi - UPPCB" 2805,3507,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 25th percentile of PM10 for November 2020.,Mizoram 2806,3508,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM2.5 in March 2022?,"Mirchaibari, Katihar - BSPCB" 2807,3509,spatial_aggregation,Which station has the highest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest average PM2.5 in February 2024.,"Central Academy for SFS, Byrnihat - PCBA" 2808,3510,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM10 in August 2020?,Kalaburagi 2809,3512,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the 2nd highest 25th percentile of PM2.5 value in June 2021.,"Lodhi Road, Delhi - IITM" 2810,3513,spatial_aggregation,Which state has the highest median PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest median PM10 during August 2022?,Jharkhand 2811,3514,spatial_aggregation,Which city has the 3rd highest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest average PM2.5 in February 2023.,Bettiah 2812,3515,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM2.5 in June 2021?,Aizawl 2813,3519,spatial_aggregation,Which station has the highest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station with the highest median PM10 in June 2024.,"Shadipur, Delhi - CPCB" 2814,3520,spatial_aggregation,Which city has the 3rd highest median PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest median PM10 in July 2021?,Munger 2815,3522,spatial_aggregation,Which station has the 2nd lowest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest median PM10 value in February 2021.,"Brahmagiri, Udupi - KSPCB" 2816,3523,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest average PM2.5 during July 2023?,"Tarapur, Silchar - PCBA" 2817,3524,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest median PM2.5 in December 2022.,"Kalyana Nagara, Chikkamagaluru - KSPCB" 2818,3525,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM2.5 in August 2018?,Tirupati 2819,3526,spatial_aggregation,Which station has the lowest median PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest median PM2.5 in May 2021.,"Ratanpura, Rupnagar - Ambuja Cements" 2820,3527,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest median PM2.5 for June 2021.,"Diwator Nagar, Koppal - KSPCB" 2821,3531,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM2.5 in January 2024.,"Bandhavgar Colony, Satna - Birla Cement" 2822,3532,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 75th percentile of PM10 value in December 2021.,Bihar Sharif 2823,3534,spatial_aggregation,Which city has the 3rd highest average PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest average PM2.5 in May 2020.,Bhiwadi 2824,3535,spatial_aggregation,Which city has the highest 25th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 25th percentile of PM10 in September 2023?,Byrnihat 2825,3536,spatial_aggregation,Which state has the lowest median PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest median PM2.5 in July 2018.,Kerala 2826,3537,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 75th percentile of PM10 for November 2021.,Uttar Pradesh 2827,3538,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM10 in July 2021?,Tripura 2828,3540,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 75th percentile of PM10 in June 2021?,"Tamaka Ind. Area, Kolar - KSPCB" 2829,3543,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest 75th percentile of PM10 during November 2024?,Mizoram 2830,3545,spatial_aggregation,Which station has the 2nd lowest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM10 in March 2024?,"Thimmalapura, Tumakuru - KSPCB" 2831,3547,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for August 2021.,"Collectorate, Jodhpur - RSPCB" 2832,3548,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile of PM2.5 in February 2023?,Navi Mumbai 2833,3549,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 75th percentile of PM2.5 in September 2023.,"DM College of Science, Imphal - Manipur PCB" 2834,3550,spatial_aggregation,Which city has the 2nd lowest median PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM10 in July 2024?,Chengalpattu 2835,3552,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 25th percentile of PM10 value in November 2022.,Meghalaya 2836,3553,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the highest 75th percentile of PM2.5 during November 2020?,"ITO, Delhi - CPCB" 2837,3554,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest 25th percentile of PM2.5 in December 2020.,Meghalaya 2838,3555,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 25th percentile of PM2.5 in December 2018?,Nashik 2839,3556,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest 25th percentile of PM2.5 in January 2024.,Mizoram 2840,3557,spatial_aggregation,Which station has the 3rd highest median PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 for May 2024.,"New Industrial Town, Faridabad - HSPCB" 2841,3558,spatial_aggregation,Which state has the highest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 75th percentile of PM10 in May 2022?,Delhi 2842,3559,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest 25th percentile of PM2.5 in September 2021.,Koppal 2843,3560,spatial_aggregation,Which station has the 2nd lowest average PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM10 in March 2020?,"Secretariat, Amaravati - APPCB" 2844,3561,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 25th percentile of PM2.5 in August 2022.,Mizoram 2845,3562,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest 25th percentile of PM2.5 value in April 2023.,Jharkhand 2846,3563,spatial_aggregation,Which city has the 3rd highest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest median PM2.5 during May 2019?,Palwal 2847,3565,spatial_aggregation,Which station has the highest average PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest average PM10 in February 2021?,"Chandni Chowk, Delhi - IITM" 2848,3567,spatial_aggregation,Which state has the highest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM10 for March 2022.,Delhi 2849,3568,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM2.5 in November 2020?,Aizawl 2850,3571,spatial_aggregation,Which city has the 3rd highest average PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM10 in August 2018.,Greater Noida 2851,3572,spatial_aggregation,Which station has the highest 25th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest 25th percentile of PM10 value in May 2019.,"North Campus, DU, Delhi - IMD" 2852,3573,spatial_aggregation,Which city has the 3rd highest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest average PM10 during February 2018?,Bhiwadi 2853,3574,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM2.5 in February 2020.,"Bandhavgar Colony, Satna - Birla Cement" 2854,3575,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 25th percentile of PM2.5 in August 2024?,Aizawl 2855,3576,spatial_aggregation,Which state has the lowest average PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest average PM2.5 in February 2022.,Mizoram 2856,3578,spatial_aggregation,Which station has the highest 75th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 75th percentile of PM10 in November 2023?,"Wazirpur, Delhi - DPCC" 2857,3580,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM10 in February 2024?,"Samanpura, Patna - BSPCB" 2858,3581,spatial_aggregation,Which station has the 3rd highest average PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM2.5 in August 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 2859,3582,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest 75th percentile of PM10 value in January 2022.,Mizoram 2860,3583,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 25th percentile of PM10 during July 2021?,Haryana 2861,3585,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 75th percentile of PM10 in May 2022?,"Kompally Municipal Office, Hyderabad - TSPCB" 2862,3586,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 75th percentile of PM10 in February 2019.,"Tamaka Ind. Area, Kolar - KSPCB" 2863,3590,spatial_aggregation,Which station has the 2nd highest average PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM2.5 in August 2021?,"Anand Vihar, Delhi - DPCC" 2864,3591,spatial_aggregation,Which state has the highest median PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM2.5 in February 2018.,Delhi 2865,3592,spatial_aggregation,Which city has the highest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest median PM10 value in June 2024.,Sri Ganganagar 2866,3593,spatial_aggregation,Which city has the lowest average PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest average PM2.5 during January 2019?,Satna 2867,3594,spatial_aggregation,Which city has the highest average PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest average PM2.5 in January 2022.,Munger 2868,3595,spatial_aggregation,Which state has the highest average PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM10 in September 2020?,Uttar Pradesh 2869,3596,spatial_aggregation,Which station has the highest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 25th percentile of PM10 in August 2019.,"Maninagar, Ahmedabad - GPCB" 2870,3597,spatial_aggregation,Which city has the 3rd highest median PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM2.5 for May 2020.,Ratlam 2871,3598,spatial_aggregation,Which state has the 2nd highest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM10 in September 2021?,Delhi 2872,3599,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state with the lowest 75th percentile of PM2.5 in May 2023.,Sikkim 2873,3600,spatial_aggregation,Which city has the 3rd highest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest median PM2.5 in October 2018?,Hapur 2874,3601,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in May 2018.,Haryana 2875,3602,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest 75th percentile of PM10 value in April 2018.,"Secretariat, Amaravati - APPCB" 2876,3603,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM10 during June 2021?,Uttar Pradesh 2877,3605,spatial_aggregation,Which state has the highest average PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM2.5 in April 2024?,Delhi 2878,3606,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest 25th percentile of PM2.5 in June 2023.,"Zero Point GICI, Gangtok - SSPCB" 2879,3608,spatial_aggregation,Which city has the 2nd lowest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest median PM10 in February 2020?,Coimbatore 2880,3609,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station with the 2nd highest 25th percentile of PM10 in January 2022.,"Mayaganj, Bhagalpur - BSPCB" 2881,3610,spatial_aggregation,Which city has the 2nd highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM2.5 in January 2022?,Siwan 2882,3611,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM10 in August 2024.,Sasaram 2883,3612,spatial_aggregation,Which station has the lowest median PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the lowest median PM10 value in March 2021.,"Tamaka Ind. Area, Kolar - KSPCB" 2884,3614,spatial_aggregation,Which city has the 2nd lowest average PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest average PM10 in October 2024.,Palkalaiperur 2885,3615,spatial_aggregation,Which station has the highest median PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest median PM10 in January 2022?,"Anand Vihar, Delhi - DPCC" 2886,3616,spatial_aggregation,Which station has the lowest median PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest median PM2.5 in September 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 2887,3618,spatial_aggregation,Which city has the lowest average PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average PM10 in November 2019?,Shillong 2888,3619,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 75th percentile of PM10 in July 2022.,"Stuart Hill, Madikeri - KSPCB" 2889,3620,spatial_aggregation,Which station has the 2nd lowest average PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest average PM10 in June 2019?,"Sanegurava Halli, Bengaluru - KSPCB" 2890,3621,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 25th percentile of PM10 in February 2018.,"Tirumala, Tirupati - APPCB" 2891,3624,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest 25th percentile of PM2.5 in September 2022.,"Navy Nagar-Colaba, Mumbai - IITM" 2892,3625,spatial_aggregation,Which state has the lowest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest median PM2.5 in April 2019?,Andhra Pradesh 2893,3627,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM2.5 for March 2024.,Rohtak 2894,3628,spatial_aggregation,Which city has the 2nd highest average PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM2.5 in May 2018?,Bhiwadi 2895,3629,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest median PM2.5 in November 2024.,"Chandni Chowk, Delhi - IITM" 2896,3630,spatial_aggregation,Which city has the highest average PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest average PM2.5 in June 2023?,Begusarai 2897,3631,spatial_aggregation,Which city has the 2nd highest average PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM2.5 in March 2021.,Singrauli 2898,3633,spatial_aggregation,Which city has the 3rd highest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest median PM10 during November 2023?,Byrnihat 2899,3634,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in February 2018.,West Bengal 2900,3635,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 25th percentile of PM2.5 in July 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2901,3637,spatial_aggregation,Which state has the lowest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM10 for May 2019.,Tamil Nadu 2902,3638,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile of PM2.5 in January 2024?,Saharsa 2903,3640,spatial_aggregation,Which station has the 3rd lowest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM10 in February 2023?,"Rohta, Agra - UPPCB" 2904,3641,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 75th percentile of PM10 in March 2022.,"Ghusuri, Howrah - WBPCB" 2905,3642,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest 25th percentile of PM2.5 value in March 2024.,Nagaland 2906,3643,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during February 2018?,"Jahangirpuri, Delhi - DPCC" 2907,3644,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest median PM2.5 in April 2023.,Aizawl 2908,3646,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest 25th percentile of PM2.5 in February 2022.,Tripura 2909,3648,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 25th percentile of PM10 in September 2020?,Shillong 2910,3649,spatial_aggregation,Which city has the 3rd highest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the 3rd highest median PM10 in March 2022.,Katihar 2911,3650,spatial_aggregation,Which station has the lowest average PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in January 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 2912,3652,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest 25th percentile of PM2.5 value in May 2020.,Ratlam 2913,3653,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest median PM2.5 during May 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 2914,3654,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest 75th percentile of PM2.5 in October 2021.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 2915,3655,spatial_aggregation,Which station has the highest average PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest average PM2.5 in February 2018?,"Jahangirpuri, Delhi - DPCC" 2916,3656,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest average PM2.5 in May 2021.,Puducherry 2917,3657,spatial_aggregation,Which city has the highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM2.5 for December 2021.,Bettiah 2918,3659,spatial_aggregation,Which state has the 2nd highest median PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM2.5 in September 2021.,Rajasthan 2919,3660,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM2.5 in December 2019?,Eloor 2920,3661,spatial_aggregation,Which state has the highest average PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM10 in June 2023.,Bihar 2921,3664,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 25th percentile of PM10 in November 2019.,"Lumpyngngad, Shillong - Meghalaya PCB" 2922,3666,spatial_aggregation,Which station has the 2nd highest median PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station that had the 2nd highest median PM10 in January 2024.,"Jahangirpuri, Delhi - DPCC" 2923,3667,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM10 for August 2018.,Delhi 2924,3668,spatial_aggregation,Which city has the highest median PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest median PM10 in April 2024?,Hajipur 2925,3669,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station with the 3rd lowest 25th percentile of PM10 in June 2022.,"Mazgaon, Mumbai - IITM" 2926,3671,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 75th percentile of PM10 in July 2022.,Delhi 2927,3672,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 75th percentile of PM2.5 value in December 2024.,Gadag 2928,3673,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd highest 75th percentile of PM2.5 during May 2023?,Begusarai 2929,3674,spatial_aggregation,Which station has the 3rd lowest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest median PM10 in March 2022.,"Velachery Res. Area, Chennai - CPCB" 2930,3675,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM10 in April 2021?,"Loni, Ghaziabad - UPPCB" 2931,3676,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station that had the highest 75th percentile of PM2.5 in April 2020.,"ITO, Delhi - CPCB" 2932,3677,spatial_aggregation,Which station has the 2nd lowest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest average PM10 for May 2024.,"Sewri, Mumbai - BMC" 2933,3678,spatial_aggregation,Which station has the 2nd highest average PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest average PM2.5 in August 2019?,"Collectorate, Jodhpur - RSPCB" 2934,3679,spatial_aggregation,Which state has the highest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state with the highest average PM2.5 in November 2020.,Delhi 2935,3680,spatial_aggregation,Which city has the highest 25th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 25th percentile of PM10 in September 2020?,Panipat 2936,3682,spatial_aggregation,Which station has the highest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest 75th percentile of PM10 value in September 2024.,"Maguda Nagar, Indore - IMC" 2937,3683,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd highest 25th percentile of PM2.5 during January 2024?,"Jahangirpuri, Delhi - DPCC" 2938,3685,spatial_aggregation,Which city has the 3rd lowest average PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest average PM10 in January 2019?,Durgapur 2939,3686,spatial_aggregation,Which state has the highest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest average PM10 in February 2024.,Tripura 2940,3688,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 75th percentile of PM10 in July 2023?,Sikkim 2941,3689,spatial_aggregation,Which city has the 2nd highest median PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest median PM10 in September 2020.,Bulandshahr 2942,3690,spatial_aggregation,Which state has the highest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM2.5 in November 2018?,Delhi 2943,3691,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 25th percentile of PM10 in January 2022.,Nandesari 2944,3692,spatial_aggregation,Which station has the 2nd lowest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the 2nd lowest median PM10 value in April 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 2945,3693,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 75th percentile of PM10 during July 2018?,"Zoo Park, Hyderabad - TSPCB" 2946,3694,spatial_aggregation,Which city has the 3rd highest average PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest average PM10 in September 2024.,Greater Noida 2947,3695,spatial_aggregation,Which state has the 2nd highest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM10 in April 2023?,Delhi 2948,3696,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest 25th percentile of PM10 in March 2018.,Andhra Pradesh 2949,3697,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM2.5 for December 2022.,"Stuart Hill, Madikeri - KSPCB" 2950,3699,spatial_aggregation,Which station has the 3rd highest average PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest average PM10 in July 2024.,"Mundka, Delhi - DPCC" 2951,3700,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 25th percentile of PM2.5 in July 2022?,Saharsa 2952,3701,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM2.5 in April 2021.,Kozhikode 2953,3702,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the 2nd lowest 25th percentile of PM2.5 value in May 2022.,Gangtok 2954,3704,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 in October 2018.,Gujarat 2955,3705,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM10 in December 2023?,"Mundka, Delhi - DPCC" 2956,3706,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city that had the lowest 75th percentile of PM10 in January 2019.,Kolar 2957,3707,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 25th percentile of PM10 for March 2018.,Pune 2958,3708,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 75th percentile of PM10 in December 2023?,Sikkim 2959,3711,spatial_aggregation,Which station has the lowest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest average PM10 in July 2022.,"Zero Point GICI, Gangtok - SSPCB" 2960,3713,spatial_aggregation,Which station has the 3rd lowest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest median PM10 during September 2023?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 2961,3714,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM2.5 in February 2019.,Delhi 2962,3716,spatial_aggregation,Which station has the 3rd highest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station that had the 3rd highest average PM2.5 in May 2021.,"Loni, Ghaziabad - UPPCB" 2963,3717,spatial_aggregation,Which state has the highest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 for March 2024.,Delhi 2964,3719,spatial_aggregation,Which station has the lowest average PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest average PM10 in October 2018.,"Talcher Coalfields,Talcher - OSPCB" 2965,3721,spatial_aggregation,Which state has the highest average PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM10 in November 2019.,Delhi 2966,3722,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest 75th percentile of PM10 value in January 2023.,Himachal Pradesh 2967,3723,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest 25th percentile of PM10 during January 2021?,Thoothukudi 2968,3724,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the lowest 75th percentile of PM2.5 in August 2022.,Aizawl 2969,3725,spatial_aggregation,Which station has the 3rd lowest median PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM10 in July 2023?,"Tarapur, Silchar - PCBA" 2970,3727,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for March 2018.,"Vikas Sadan, Gurugram - HSPCB" 2971,3729,spatial_aggregation,Which station has the lowest median PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest median PM2.5 in March 2024.,"Bhelupur, Varanasi - UPPCB" 2972,3730,spatial_aggregation,Which state has the 3rd lowest average PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest average PM10 in June 2024?,Meghalaya 2973,3731,spatial_aggregation,Which station has the 2nd highest median PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM10 in August 2023.,"LGBI Airport, Guwahati - PCBA" 2974,3732,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 25th percentile of PM10 value in July 2021.,Rajasthan 2975,3733,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 75th percentile of PM2.5 during June 2019?,"Bandhavgar Colony, Satna - Birla Cement" 2976,3734,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest average PM2.5 in March 2020.,Satna 2977,3735,spatial_aggregation,Which city has the 2nd highest median PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in November 2022?,Darbhanga 2978,3736,spatial_aggregation,Which state has the lowest average PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state that had the lowest average PM2.5 in September 2024.,Mizoram 2979,3737,spatial_aggregation,Which city has the 3rd highest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM2.5 for May 2021.,Jodhpur 2980,3738,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile of PM2.5 in April 2023?,Byrnihat 2981,3739,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city with the 3rd lowest 75th percentile of PM10 in January 2019.,Chandrapur 2982,3741,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest average PM2.5 in December 2023.,Mizoram 2983,3742,spatial_aggregation,Which station has the 3rd lowest median PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the 3rd lowest median PM10 value in March 2019.,"Sanegurava Halli, Bengaluru - KSPCB" 2984,3743,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 75th percentile of PM10 during January 2019?,"Sanegurava Halli, Bengaluru - KSPCB" 2985,3744,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM2.5 in October 2024.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 2986,3745,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 25th percentile of PM2.5 in April 2023?,"MIT-Daudpur Kothi, Muzaffarpur - BSPCB" 2987,3746,spatial_aggregation,Which state has the 2nd lowest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest median PM10 in November 2023.,Mizoram 2988,3748,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM2.5 in May 2020?,Uttar Pradesh 2989,3749,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest 25th percentile of PM2.5 in March 2018.,Bihar 2990,3750,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest average PM2.5 in August 2020?,"Deshpande Nagar, Hubballi - KSPCB" 2991,3751,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM10 in January 2019.,Howrah 2992,3752,spatial_aggregation,Which state has the 2nd lowest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest median PM10 value in August 2021.,Mizoram 2993,3753,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during September 2019?,"Dwarka-Sector 8, Delhi - DPCC" 2994,3754,spatial_aggregation,Which city has the highest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest average PM2.5 in October 2020.,Charkhi Dadri 2995,3756,spatial_aggregation,Which state has the 3rd highest median PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest median PM10 in March 2020.,Bihar 2996,3757,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 25th percentile of PM10 for February 2021.,"Loni, Ghaziabad - UPPCB" 2997,3758,spatial_aggregation,Which city has the highest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest average PM10 in December 2024?,Byrnihat 2998,3759,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest 25th percentile of PM2.5 in August 2023.,"Khadakpada, Kalyan - MPCB" 2999,3760,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest 75th percentile of PM10 in February 2020?,"Airoli, Navi Mumbai - MPCB" 3000,3762,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the 2nd highest 75th percentile of PM10 value in July 2023.,Delhi 3001,3763,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 75th percentile of PM10 during January 2020?,"Dwarka-Sector 8, Delhi - DPCC" 3002,3764,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM2.5 in November 2019.,"IGSC Planetarium Complex, Patna - BSPCB" 3003,3765,spatial_aggregation,Which city has the highest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in September 2021?,Yamuna Nagar 3004,3768,spatial_aggregation,Which state has the 3rd highest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest median PM10 in August 2021?,Rajasthan 3005,3770,spatial_aggregation,Which station has the lowest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest average PM2.5 in November 2020?,"Udyogamandal, Eloor - Kerala PCB" 3006,3771,spatial_aggregation,Which state has the highest average PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM2.5 in February 2019.,Bihar 3007,3772,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest 25th percentile of PM2.5 value in April 2022.,"Loni, Ghaziabad - UPPCB" 3008,3773,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during August 2023?,"Anand Vihar, Delhi - DPCC" 3009,3776,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest 25th percentile of PM2.5 in October 2023.,"Tarapur, Silchar - PCBA" 3010,3777,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest average PM2.5 for June 2018.,Hubballi 3011,3778,spatial_aggregation,Which station has the highest average PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest average PM10 in October 2020?,"Mundka, Delhi - DPCC" 3012,3779,spatial_aggregation,Which city has the lowest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest average PM2.5 in September 2019.,Rajamahendravaram 3013,3780,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest 75th percentile of PM2.5 in November 2018?,Satna 3014,3781,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 75th percentile of PM10 in May 2020.,Singrauli 3015,3782,spatial_aggregation,Which station has the highest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest median PM2.5 value in November 2022.,"Gandak Colony, Motihari - BSPCB" 3016,3784,spatial_aggregation,Which city has the highest median PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest median PM2.5 in May 2023.,Byrnihat 3017,3785,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest 75th percentile of PM2.5 in July 2024?,"Central Academy for SFS, Byrnihat - PCBA" 3018,3787,spatial_aggregation,Which city has the 3rd lowest average PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest average PM10 for July 2020.,Mysuru 3019,3788,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM2.5 in June 2018?,Delhi 3020,3790,spatial_aggregation,Which station has the lowest median PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest median PM2.5 in April 2021?,"Velachery Res. Area, Chennai - CPCB" 3021,3792,spatial_aggregation,Which state has the 3rd highest median PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the 3rd highest median PM2.5 value in July 2023.,Delhi 3022,3793,spatial_aggregation,Which city has the 2nd highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd highest average PM2.5 during March 2020?,Guwahati 3023,3795,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in January 2021?,Ghaziabad 3024,3797,spatial_aggregation,Which state has the lowest median PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM2.5 for February 2019.,Tamil Nadu 3025,3799,spatial_aggregation,Which city has the highest 75th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the highest 75th percentile of PM10 in March 2023.,Byrnihat 3026,3800,spatial_aggregation,Which state has the 2nd highest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest median PM2.5 in November 2019?,Uttar Pradesh 3027,3801,spatial_aggregation,Which state has the lowest average PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest average PM10 in November 2020.,Meghalaya 3028,3802,spatial_aggregation,Which city has the 2nd highest average PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the 2nd highest average PM2.5 value in January 2021.,Noida 3029,3803,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd highest 25th percentile of PM10 during July 2021?,"Chandni Chowk, Delhi - IITM" 3030,3804,spatial_aggregation,Which station has the 3rd highest median PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest median PM10 in June 2021.,"Nerul, Navi Mumbai - MPCB" 3031,3806,spatial_aggregation,Which state has the 3rd highest average PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state that had the 3rd highest average PM2.5 in December 2020.,Bihar 3032,3807,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM10 for January 2022.,Delhi 3033,3808,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 75th percentile of PM2.5 in March 2022?,"Loni, Ghaziabad - UPPCB" 3034,3809,spatial_aggregation,Which city has the lowest average PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city with the lowest average PM2.5 in April 2018.,Rajamahendravaram 3035,3810,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 75th percentile of PM10 in September 2021?,Delhi 3036,3811,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest 75th percentile of PM10 in January 2019.,"North Campus, DU, Delhi - IMD" 3037,3812,spatial_aggregation,Which state has the highest 75th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the highest 75th percentile of PM10 value in December 2020.,Uttar Pradesh 3038,3813,spatial_aggregation,Which state has the highest 75th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the highest 75th percentile of PM10 during November 2019?,Delhi 3039,3816,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state that had the 3rd lowest 75th percentile of PM2.5 in July 2021.,Arunachal Pradesh 3040,3817,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 25th percentile of PM2.5 for July 2021.,"GIDC, Nandesari - Nandesari Ind. Association" 3041,3818,spatial_aggregation,Which station has the 3rd highest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest median PM2.5 in November 2019?,"Loni, Ghaziabad - UPPCB" 3042,3819,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station with the 2nd lowest 75th percentile of PM2.5 in June 2024.,"Diwator Nagar, Koppal - KSPCB" 3043,3820,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 25th percentile of PM10 in April 2020?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3044,3821,spatial_aggregation,Which station has the 2nd highest median PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM10 in July 2018.,"Wazirpur, Delhi - DPCC" 3045,3822,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the 2nd lowest median PM2.5 value in August 2024.,Manipur 3046,3824,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 75th percentile of PM2.5 in January 2024.,Sikkim 3047,3825,spatial_aggregation,Which city has the 3rd lowest median PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM10 in September 2021?,Mangalore 3048,3826,spatial_aggregation,Which station has the lowest average PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest average PM10 in April 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3049,3828,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM10 in March 2020?,Gujarat 3050,3829,spatial_aggregation,Which city has the 2nd highest average PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city with the 2nd highest average PM10 in March 2024.,Sri Ganganagar 3051,3830,spatial_aggregation,Which city has the 2nd lowest average PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest average PM10 in January 2019?,Vijayawada 3052,3831,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM2.5 in January 2024.,Mandikhera 3053,3832,spatial_aggregation,Which state has the 3rd lowest median PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest median PM10 value in April 2023.,Arunachal Pradesh 3054,3833,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd lowest 75th percentile of PM10 during September 2021?,"Brahmagiri, Udupi - KSPCB" 3055,3835,spatial_aggregation,Which state has the highest average PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM2.5 in March 2023?,Jharkhand 3056,3836,spatial_aggregation,Which station has the 3rd lowest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station that had the 3rd lowest median PM10 in November 2021.,"Panchal Nagar, Gadag - KSPCB" 3057,3837,spatial_aggregation,Which city has the 2nd highest median PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM10 for December 2023.,Hanumangarh 3058,3838,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest 25th percentile of PM2.5 in May 2021?,"Sector 30, Faridabad - HSPCB" 3059,3839,spatial_aggregation,Which station has the lowest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station with the lowest median PM10 in November 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 3060,3841,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 75th percentile of PM10 in June 2020.,"Hebbal 1st Stage, Mysuru - KSPCB" 3061,3843,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM10 during April 2020?,Odisha 3062,3844,spatial_aggregation,Which station has the lowest median PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest median PM2.5 in May 2023.,"Zero Point GICI, Gangtok - SSPCB" 3063,3845,spatial_aggregation,Which state has the highest median PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in April 2020?,Odisha 3064,3846,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state that had the 2nd lowest average PM2.5 in November 2018.,Andhra Pradesh 3065,3847,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 for July 2021.,"ITO, Delhi - CPCB" 3066,3850,spatial_aggregation,Which state has the 2nd highest average PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest average PM10 in March 2021?,Assam 3067,3851,spatial_aggregation,Which city has the 3rd lowest average PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM10 in March 2021.,Shivamogga 3068,3852,spatial_aggregation,Which city has the highest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the highest 25th percentile of PM10 value in November 2024.,Delhi 3069,3854,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM10 in December 2024.,Tripura 3070,3857,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 for January 2019.,Satna 3071,3858,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM10 in August 2018?,Greater Noida 3072,3860,spatial_aggregation,Which state has the 3rd highest average PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest average PM2.5 in June 2019?,Delhi 3073,3861,spatial_aggregation,Which station has the lowest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest average PM10 in April 2024.,"Crescent University, Chengalpattu - TNPCB" 3074,3862,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the 3rd lowest 25th percentile of PM2.5 value in September 2020.,Eloor 3075,3863,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest 25th percentile of PM10 during September 2020?,Aizawl 3076,3865,spatial_aggregation,Which city has the highest average PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest average PM10 in September 2020?,Bhiwadi 3077,3866,spatial_aggregation,Which station has the lowest average PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station that had the lowest average PM10 in January 2021.,"Tamaka Ind. Area, Kolar - KSPCB" 3078,3867,spatial_aggregation,Which station has the 3rd lowest median PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest median PM10 for August 2018.,"Hebbal, Bengaluru - KSPCB" 3079,3868,spatial_aggregation,Which state has the 3rd highest median PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest median PM2.5 in April 2020?,Jharkhand 3080,3869,spatial_aggregation,Which state has the 2nd highest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the 2nd highest median PM10 in September 2018.,Uttar Pradesh 3081,3870,spatial_aggregation,Which state has the highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in July 2022?,Delhi 3082,3871,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 75th percentile of PM2.5 in September 2020.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 3083,3872,spatial_aggregation,Which state has the 3rd lowest median PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the 3rd lowest median PM10 value in May 2018.,Andhra Pradesh 3084,3873,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd lowest 25th percentile of PM2.5 during November 2019?,Kerala 3085,3874,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 75th percentile of PM10 in February 2022.,Shillong 3086,3875,spatial_aggregation,Which station has the 3rd highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in July 2022?,"Govt. High School Shikarpur, Patna - BSPCB" 3087,3876,spatial_aggregation,Which state has the 2nd highest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest median PM10 in November 2020.,Uttar Pradesh 3088,3877,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 75th percentile of PM2.5 for July 2022.,"Gobind Pura, Yamuna Nagar - HSPCB" 3089,3878,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median PM2.5 in January 2019?,Ludhiana 3090,3879,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the 3rd highest 25th percentile of PM10 in October 2021.,"Shadipur, Delhi - CPCB" 3091,3880,spatial_aggregation,Which state has the highest median PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM10 in May 2018?,Uttar Pradesh 3092,3881,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 25th percentile of PM2.5 in October 2024.,Aizawl 3093,3882,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the highest 75th percentile of PM2.5 value in April 2022.,"Loni, Ghaziabad - UPPCB" 3094,3884,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest average PM2.5 in July 2020.,Mysuru 3095,3885,spatial_aggregation,Which station has the 3rd lowest median PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest median PM10 in February 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 3096,3886,spatial_aggregation,Which state has the highest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state that had the highest median PM10 in February 2022.,Delhi 3097,3887,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 25th percentile of PM10 for October 2019.,Meghalaya 3098,3888,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM10 in June 2019?,Delhi 3099,3890,spatial_aggregation,Which city has the 2nd highest average PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest average PM10 in June 2022?,Hapur 3100,3891,spatial_aggregation,Which city has the 2nd highest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM2.5 in January 2023.,Byrnihat 3101,3893,spatial_aggregation,Which city has the lowest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the lowest median PM10 during March 2022?,Maihar 3102,3895,spatial_aggregation,Which state has the 3rd lowest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest median PM10 in March 2022?,Chhattisgarh 3103,3896,spatial_aggregation,Which state has the 2nd highest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state that had the 2nd highest median PM10 in September 2023.,Himachal Pradesh 3104,3897,spatial_aggregation,Which city has the highest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest average PM10 for April 2024.,Byrnihat 3105,3898,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM10 in October 2021?,Jharkhand 3106,3899,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state with the 3rd lowest 75th percentile of PM10 in June 2020.,Assam 3107,3901,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 25th percentile of PM2.5 in May 2020.,Aizawl 3108,3902,spatial_aggregation,Which state has the lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest median PM2.5 value in March 2019.,Andhra Pradesh 3109,3903,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest 75th percentile of PM2.5 during August 2018?,Chikkaballapur 3110,3906,spatial_aggregation,Which state has the lowest average PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In February 2024, identify the state with the lowest average PM2.5.",Jammu and Kashmir 3111,3908,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 25th percentile of PM2.5 in June 2024.,Aizawl 3112,3909,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In June 2020, report the station with the lowest 75th percentile of PM10.","Sikulpuikawn, Aizawl - Mizoram PCB" 3113,3910,spatial_aggregation,Which city has the lowest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest average PM2.5 in November 2020?,Eloor 3114,3911,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 25th percentile of PM10 in February 2023.,Jammu and Kashmir 3115,3912,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In March 2018, which station exhibited the 2nd lowest 25th percentile of PM2.5?","BTM Layout, Bengaluru - CPCB" 3116,3913,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 25th percentile of PM2.5 in May 2019.,"Bandhavgar Colony, Satna - Birla Cement" 3117,3914,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In July 2021, report the city with the highest 75th percentile of PM2.5.",Nandesari 3118,3915,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city displayed the 2nd lowest 25th percentile of PM2.5 in July 2022?,Gangtok 3119,3916,spatial_aggregation,Which state has the highest 25th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM10 in July 2024.,Delhi 3120,3917,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In March 2024, which city had the 3rd lowest 25th percentile of PM10?",Maihar 3121,3918,spatial_aggregation,Which city has the lowest average PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest average PM10 in July 2024.,Chengalpattu 3122,3919,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In June 2018, identify the city with the 2nd lowest 25th percentile of PM10.",Thiruvananthapuram 3123,3920,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the lowest 25th percentile of PM10 in March 2021?,"Tamaka Ind. Area, Kolar - KSPCB" 3124,3921,spatial_aggregation,Which station has the 3rd lowest average PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM10 in August 2020.,"Karve Road, Pune - MPCB" 3125,3922,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In November 2023, report the city with the 3rd lowest 75th percentile of PM2.5.",Sivasagar 3126,3923,spatial_aggregation,Which state has the lowest average PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state showed the lowest average PM2.5 in February 2021?,Meghalaya 3127,3924,spatial_aggregation,Which station has the 2nd highest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In September 2023, which station recorded the 2nd highest average PM10?","Central Academy for SFS, Byrnihat - PCBA" 3128,3925,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 75th percentile of PM10 in December 2019.,Noida 3129,3927,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In September 2018, identify the station with the 2nd lowest 25th percentile of PM2.5.","BWSSB Kadabesanahalli, Bengaluru - CPCB" 3130,3929,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In September 2024, report the station with the 3rd lowest 25th percentile of PM10.","Diwator Nagar, Koppal - KSPCB" 3131,3930,spatial_aggregation,Which state has the highest average PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM2.5 in June 2019.,Haryana 3132,3931,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In December 2024, which city displayed the lowest 25th percentile of PM2.5?",Aizawl 3133,3932,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 75th percentile of PM2.5 in March 2022.,Bihar Sharif 3134,3933,spatial_aggregation,Which city has the 2nd lowest average PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In May 2023, identify the city with the 2nd lowest average PM10.",Udupi 3135,3934,spatial_aggregation,Which station has the 2nd lowest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest median PM10 in October 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 3136,3935,spatial_aggregation,Which state has the 3rd lowest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest average PM10 in June 2018.,Maharashtra 3137,3936,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In February 2022, report the city with the 3rd highest 25th percentile of PM10.",Durgapur 3138,3937,spatial_aggregation,Which state has the lowest average PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the lowest average PM2.5 in November 2021?,Mizoram 3139,3938,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In November 2022, which station showed the 3rd highest 25th percentile of PM2.5?","Chitragupta Nagar, Siwan - BSPCB" 3140,3939,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM2.5 in August 2022.,"Bandhavgar Colony, Satna - Birla Cement" 3141,3940,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In November 2022, identify the city with the 2nd highest 75th percentile of PM10.",Darbhanga 3142,3941,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 75th percentile of PM10 in January 2020?,Meghalaya 3143,3944,spatial_aggregation,Which state has the 2nd highest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM2.5 in May 2019.,Uttar Pradesh 3144,3945,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In May 2021, identify the state with the 2nd lowest median PM2.5.",Arunachal Pradesh 3145,3947,spatial_aggregation,Which state has the 3rd lowest average PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest average PM10 in December 2022.,Meghalaya 3146,3948,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In April 2019, report the station with the 2nd lowest 75th percentile of PM10.","Ratanpura, Rupnagar - Ambuja Cements" 3147,3949,spatial_aggregation,Which city has the highest 25th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 25th percentile of PM10 in October 2021?,Ghaziabad 3148,3950,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In January 2021, identify the state with the 3rd lowest 25th percentile of PM2.5.",Karnataka 3149,3951,spatial_aggregation,Which city has the 2nd highest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM2.5 in July 2020.,Mandi Gobindgarh 3150,3952,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In December 2022, which city registered the lowest 25th percentile of PM10?",Shillong 3151,3953,spatial_aggregation,Which station has the lowest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM10 in September 2023.,"Brahmagiri, Udupi - KSPCB" 3152,3955,spatial_aggregation,Which city has the highest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest 75th percentile of PM10 in September 2021?,Srinagar 3153,3956,spatial_aggregation,Which state has the highest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In April 2020, identify the state with the highest 75th percentile of PM10.",Uttar Pradesh 3154,3957,spatial_aggregation,Which station has the 2nd highest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest average PM2.5 in May 2024.,"Shadipur, Delhi - CPCB" 3155,3958,spatial_aggregation,Which state has the 3rd highest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In April 2022, which state recorded the 3rd highest average PM10?",Bihar 3156,3959,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 25th percentile of PM2.5 in April 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3157,3960,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In May 2023, report the state with the 2nd lowest average PM2.5.",Mizoram 3158,3961,spatial_aggregation,Which city has the 3rd highest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city exhibited the 3rd highest median PM2.5 in August 2024?,Sri Ganganagar 3159,3962,spatial_aggregation,Which state has the highest median PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In January 2021, identify the state with the highest median PM10.",Delhi 3160,3964,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In September 2024, which city displayed the 2nd lowest 75th percentile of PM10?",Koppal 3161,3966,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In September 2022, report the state with the 2nd lowest average PM2.5.",Sikkim 3162,3968,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In November 2022, identify the state with the 3rd lowest average PM2.5.",Meghalaya 3163,3969,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 25th percentile of PM2.5 in November 2019.,Meghalaya 3164,3970,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In June 2019, which city registered the 2nd lowest 75th percentile of PM2.5?",Eloor 3165,3971,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 25th percentile of PM2.5 in May 2019.,Bhiwadi 3166,3972,spatial_aggregation,Which city has the 3rd highest median PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In March 2018, report the city with the 3rd highest median PM10.",Bhiwadi 3167,3973,spatial_aggregation,Which city has the 2nd lowest average PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city showed the 2nd lowest average PM10 in October 2023?,Silchar 3168,3974,spatial_aggregation,Which station has the highest 25th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In March 2021, identify the station with the highest 25th percentile of PM10.","Bawana, Delhi - DPCC" 3169,3975,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 25th percentile of PM10 in May 2021.,Fatehabad 3170,3976,spatial_aggregation,Which station has the lowest median PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In August 2019, which station recorded the lowest median PM10?","Hardev Nagar, Bathinda - PPCB" 3171,3977,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM2.5 in December 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 3172,3978,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In March 2020, report the city with the 3rd highest 25th percentile of PM10.",Faridabad 3173,3979,spatial_aggregation,Which state has the 2nd lowest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state exhibited the 2nd lowest median PM10 in November 2018?,Andhra Pradesh 3174,3980,spatial_aggregation,Which state has the 2nd highest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In September 2023, identify the state with the 2nd highest average PM2.5.",Tripura 3175,3981,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM10 in June 2019.,Baghpat 3176,3982,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In June 2024, which city displayed the 3rd lowest 75th percentile of PM10?",Ramanagara 3177,3983,spatial_aggregation,Which city has the 2nd lowest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM10 in May 2021.,Kolar 3178,3984,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In October 2019, report the station with the highest 75th percentile of PM2.5.","Sanjay Nagar, Ghaziabad - UPPCB" 3179,3985,spatial_aggregation,Which state has the 3rd highest average PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest average PM2.5 in October 2023?,Haryana 3180,3986,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In July 2018, identify the station with the 3rd highest 25th percentile of PM2.5.","Manali, Chennai - CPCB" 3181,3987,spatial_aggregation,Which city has the lowest median PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest median PM2.5 in September 2022.,Aizawl 3182,3988,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In November 2018, which station registered the 2nd lowest 75th percentile of PM2.5?","MIDC Khutala, Chandrapur - MPCB" 3183,3990,spatial_aggregation,Which state has the 3rd highest median PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In June 2021, report the state with the 3rd highest median PM10.",Uttar Pradesh 3184,3991,spatial_aggregation,Which city has the lowest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest median PM10 in December 2024?,Shillong 3185,3993,spatial_aggregation,Which station has the 2nd highest average PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest average PM2.5 in January 2020.,"Jahangirpuri, Delhi - DPCC" 3186,3994,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In August 2022, which state recorded the 2nd lowest 25th percentile of PM10?",Odisha 3187,3995,spatial_aggregation,Which city has the lowest median PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM2.5 in April 2019.,Rajamahendravaram 3188,3996,spatial_aggregation,Which station has the highest average PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In December 2021, report the station with the highest average PM10.","Anand Vihar, Delhi - DPCC" 3189,3997,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state exhibited the 2nd highest 25th percentile of PM2.5 in April 2024?,Delhi 3190,3999,spatial_aggregation,Which state has the 2nd highest average PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM10 in June 2020.,Delhi 3191,4000,spatial_aggregation,Which state has the 3rd highest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In July 2024, which state displayed the 3rd highest average PM2.5?",Punjab 3192,4001,spatial_aggregation,Which station has the 3rd lowest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM10 in July 2023.,"Naubad, Bidar - KSPCB" 3193,4002,spatial_aggregation,Which city has the 2nd highest median PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In November 2019, report the city with the 2nd highest median PM2.5.",Patna 3194,4003,spatial_aggregation,Which city has the 2nd lowest average PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest average PM10 in October 2022?,Sivasagar 3195,4004,spatial_aggregation,Which station has the lowest median PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In January 2018, identify the station with the lowest median PM2.5.","BWSSB Kadabesanahalli, Bengaluru - CPCB" 3196,4006,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In September 2023, which state registered the 2nd lowest average PM2.5?",Sikkim 3197,4007,spatial_aggregation,Which state has the 3rd lowest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest median PM10 in February 2023.,Puducherry 3198,4008,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In August 2022, report the station with the 3rd highest 25th percentile of PM10.","Police Line, Saharsa - BSPCB" 3199,4010,spatial_aggregation,Which station has the highest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In July 2021, identify the station with the highest 25th percentile of PM10.","Town Hall, Munger - BSPCB" 3200,4011,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 75th percentile of PM2.5 in August 2019.,Manesar 3201,4012,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In May 2024, which state recorded the 3rd lowest average PM2.5?",Sikkim 3202,4013,spatial_aggregation,Which station has the lowest average PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM10 in July 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 3203,4014,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In September 2023, report the city with the highest 75th percentile of PM2.5.",Byrnihat 3204,4015,spatial_aggregation,Which city has the 2nd lowest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city exhibited the 2nd lowest average PM10 in August 2024?,Koppal 3205,4016,spatial_aggregation,Which station has the 2nd highest median PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In June 2019, identify the station with the 2nd highest median PM2.5.","Shadipur, Delhi - CPCB" 3206,4017,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest average PM2.5 in May 2022.,Manipur 3207,4018,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In May 2018, which city displayed the 3rd highest 75th percentile of PM2.5?",Lucknow 3208,4020,spatial_aggregation,Which city has the highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In January 2022, report the city with the highest median PM2.5.",Munger 3209,4021,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM2.5 in November 2018?,"Rohini, Delhi - DPCC" 3210,4023,spatial_aggregation,Which city has the 3rd highest average PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM2.5 in March 2020.,Muzaffarpur 3211,4024,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In October 2022, which state registered the 2nd lowest median PM2.5?",Sikkim 3212,4026,spatial_aggregation,Which city has the 3rd highest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In May 2024, report the city with the 3rd highest average PM10.",Faridabad 3213,4027,spatial_aggregation,Which city has the lowest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest average PM2.5 in September 2020?,Aizawl 3214,4028,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In August 2022, identify the station with the 2nd highest 75th percentile of PM2.5.","Kareemganj, Gaya - BSPCB" 3215,4029,spatial_aggregation,Which city has the 3rd lowest median PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM10 in February 2019.,Khanna 3216,4031,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 75th percentile of PM2.5 in July 2019.,Jodhpur 3217,4032,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In February 2023, report the state with the 3rd highest 75th percentile of PM10.",Assam 3218,4033,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station exhibited the 2nd lowest 75th percentile of PM2.5 in February 2023?,"Sector-3B Avas Vikas Colony, Agra - UPPCB" 3219,4034,spatial_aggregation,Which station has the highest average PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In May 2020, identify the station with the highest average PM2.5.","Punjabi Bagh, Delhi - DPCC" 3220,4037,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM10 in July 2023.,Delhi 3221,4038,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In December 2022, report the state with the lowest 75th percentile of PM10.",Arunachal Pradesh 3222,4039,spatial_aggregation,Which city has the lowest median PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM10 in February 2024?,Tumakuru 3223,4041,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM2.5 in April 2024.,"IESD Banaras Hindu University, Varanasi - UPPCB" 3224,4043,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest average PM2.5 in August 2019.,Bathinda 3225,4044,spatial_aggregation,Which state has the 2nd lowest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In November 2024, report the state with the 2nd lowest average PM10.",Mizoram 3226,4046,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In August 2020, identify the station with the 3rd lowest median PM2.5.","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 3227,4047,spatial_aggregation,Which state has the lowest median PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in September 2022.,Mizoram 3228,4048,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In January 2021, which station recorded the 3rd highest 25th percentile of PM10?","DRM Office Danapur, Patna - BSPCB" 3229,4049,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 75th percentile of PM2.5 in April 2019.,Tamil Nadu 3230,4051,spatial_aggregation,Which station has the 2nd highest average PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station exhibited the 2nd highest average PM10 in April 2020?,"Manali Village, Chennai - TNPCB" 3231,4052,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In August 2019, identify the state with the lowest 25th percentile of PM2.5.",Chandigarh 3232,4053,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 75th percentile of PM2.5 in May 2021.,"Devaraj Urs Badavane, Davanagere - KSPCB" 3233,4054,spatial_aggregation,Which state has the highest 75th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In September 2019, which state displayed the highest 75th percentile of PM10?",Delhi 3234,4055,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest average PM2.5 in June 2024.,Koppal 3235,4056,spatial_aggregation,Which station has the 2nd highest median PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In March 2023, report the station with the 2nd highest median PM10.","SIDCO Kurichi, Coimbatore - TNPCB" 3236,4058,spatial_aggregation,Which station has the 2nd highest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In September 2023, identify the station with the 2nd highest median PM10.","Science Center, Surat - SMC" 3237,4059,spatial_aggregation,Which station has the 3rd lowest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest average PM10 in April 2019.,"PWD Grounds, Vijayawada - APPCB" 3238,4061,spatial_aggregation,Which state has the highest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 in April 2023.,Bihar 3239,4062,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In January 2018, report the state with the 3rd lowest 25th percentile of PM2.5.",Tamil Nadu 3240,4063,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the 3rd lowest average PM2.5 in May 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 3241,4064,spatial_aggregation,Which station has the highest 25th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In May 2020, identify the station with the highest 25th percentile of PM10.","GIDC, Nandesari - Nandesari Ind. Association" 3242,4065,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM2.5 in March 2020.,Charkhi Dadri 3243,4066,spatial_aggregation,Which city has the highest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In January 2019, which city recorded the highest 25th percentile of PM10?",Talcher 3244,4068,spatial_aggregation,Which station has the 2nd highest median PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In May 2023, report the station with the 2nd highest median PM2.5.","Muradpur, Patna - BSPCB" 3245,4069,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city exhibited the 2nd highest 25th percentile of PM2.5 in February 2019?,Patna 3246,4070,spatial_aggregation,Which station has the highest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In November 2021, identify the station with the highest median PM10.","Sector 11, Faridabad - HSPCB" 3247,4071,spatial_aggregation,Which state has the 2nd highest average PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM10 in August 2023.,Himachal Pradesh 3248,4072,spatial_aggregation,Which city has the 3rd highest median PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In March 2021, which city displayed the 3rd highest median PM10?",Bhiwadi 3249,4073,spatial_aggregation,Which state has the 3rd lowest average PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest average PM10 in September 2022.,Meghalaya 3250,4074,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In March 2020, report the state with the 2nd lowest median PM2.5.",Andhra Pradesh 3251,4077,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest median PM2.5 in October 2018.,Thiruvananthapuram 3252,4078,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In January 2018, which state registered the 2nd lowest 75th percentile of PM2.5?",Maharashtra 3253,4079,spatial_aggregation,Which state has the highest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM2.5 in February 2020.,Delhi 3254,4080,spatial_aggregation,Which station has the lowest average PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In April 2022, report the station with the lowest average PM2.5.","Perungudi, Chennai - TNPCB" 3255,4081,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 75th percentile of PM10 in May 2022?,Sonipat 3256,4082,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In September 2020, identify the state with the 3rd lowest median PM2.5.",Kerala 3257,4083,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 75th percentile of PM10 in October 2019.,"Anand Vihar, Delhi - DPCC" 3258,4084,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In May 2022, which city recorded the highest 25th percentile of PM2.5?",Bhiwadi 3259,4085,spatial_aggregation,Which city has the 3rd highest median PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM2.5 in October 2024.,Hapur 3260,4086,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In February 2021, report the city with the 2nd highest 25th percentile of PM10.",Greater Noida 3261,4088,spatial_aggregation,Which station has the highest 25th percentile of PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In February 2018, identify the station with the highest 25th percentile of PM10.","Karve Road, Pune - MPCB" 3262,4090,spatial_aggregation,Which city has the lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In March 2019, which city displayed the lowest median PM2.5?",Satna 3263,4091,spatial_aggregation,Which city has the lowest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM10 in February 2022.,Nandesari 3264,4092,spatial_aggregation,Which city has the 2nd lowest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In March 2019, report the city with the 2nd lowest average PM10.",Rajamahendravaram 3265,4093,spatial_aggregation,Which state has the lowest average PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest average PM10 in April 2018?,Kerala 3266,4095,spatial_aggregation,Which state has the lowest median PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in April 2024.,Puducherry 3267,4096,spatial_aggregation,Which state has the 3rd highest average PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In January 2022, which state registered the 3rd highest average PM2.5?",Tripura 3268,4097,spatial_aggregation,Which city has the 3rd highest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM2.5 in December 2022.,Darbhanga 3269,4098,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In August 2019, report the city with the 2nd lowest 25th percentile of PM10.",Kolar 3270,4099,spatial_aggregation,Which state has the highest 25th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest 25th percentile of PM10 in June 2020?,Uttar Pradesh 3271,4101,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest 75th percentile of PM2.5 in September 2020.,"Mini Secretariat, Charkhi Dadri - HSPCB" 3272,4103,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest median PM2.5 in August 2018.,Chikkaballapur 3273,4104,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In June 2024, report the city with the 2nd lowest 75th percentile of PM2.5.",Koppal 3274,4105,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city exhibited the 3rd lowest average PM2.5 in September 2023?,Imphal 3275,4106,spatial_aggregation,Which state has the highest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In November 2023, identify the state with the highest median PM10.",Delhi 3276,4107,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM2.5 in April 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3277,4108,spatial_aggregation,Which station has the highest average PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In March 2021, which station displayed the highest average PM2.5?","Mundka, Delhi - DPCC" 3278,4109,spatial_aggregation,Which city has the 2nd highest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM10 in July 2022.,Saharsa 3279,4110,spatial_aggregation,Which city has the highest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In May 2024, report the city with the highest average PM2.5.",Faridabad 3280,4111,spatial_aggregation,Which station has the lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest median PM2.5 in March 2019?,"Bandhavgar Colony, Satna - Birla Cement" 3281,4112,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In January 2020, identify the state with the 2nd lowest 25th percentile of PM10.",Tamil Nadu 3282,4113,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 25th percentile of PM10 in March 2020.,"SIDCO Kurichi, Coimbatore - TNPCB" 3283,4114,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In May 2019, which city registered the 2nd lowest average PM2.5?",Mumbai 3284,4115,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 75th percentile of PM10 in November 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 3285,4118,spatial_aggregation,Which city has the 3rd highest average PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In August 2021, identify the city with the 3rd highest average PM10.",Sonipat 3286,4119,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in June 2024.,Haryana 3287,4120,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In September 2020, which state recorded the 3rd highest 75th percentile of PM10?",Haryana 3288,4122,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In May 2020, report the city with the 2nd lowest 75th percentile of PM2.5.",Aurangabad 3289,4123,spatial_aggregation,Which station has the 3rd lowest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station exhibited the 3rd lowest median PM10 in August 2021?,"Kadri, Mangalore - KSPCB" 3290,4124,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In August 2020, identify the state with the lowest 25th percentile of PM10.",Meghalaya 3291,4126,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In February 2018, which city displayed the 2nd lowest 25th percentile of PM2.5?",Bengaluru 3292,4127,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 75th percentile of PM2.5 in November 2024.,Ghaziabad 3293,4128,spatial_aggregation,Which state has the 2nd highest median PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In May 2022, report the state with the 2nd highest median PM2.5.",Haryana 3294,4129,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM2.5 in May 2020?,Maharashtra 3295,4130,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In June 2019, identify the city with the lowest 25th percentile of PM10.",Eloor 3296,4131,spatial_aggregation,Which state has the 2nd lowest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest median PM10 in June 2022.,Mizoram 3297,4133,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 25th percentile of PM2.5 in November 2022.,Gangtok 3298,4134,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In February 2021, report the city with the 3rd highest 25th percentile of PM2.5.",Noida 3299,4135,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 75th percentile of PM10 in April 2019?,Kerala 3300,4136,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In November 2022, identify the station with the 2nd highest 75th percentile of PM10.","Anand Vihar, Delhi - DPCC" 3301,4138,spatial_aggregation,Which station has the highest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In March 2019, which station recorded the highest average PM10?","Sirifort, Delhi - CPCB" 3302,4139,spatial_aggregation,Which station has the 2nd lowest median PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest median PM10 in August 2023.,"Panchal Nagar, Gadag - KSPCB" 3303,4140,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In January 2022, report the station with the 2nd lowest median PM2.5.","GIDC, Nandesari - Nandesari Ind. Association" 3304,4141,spatial_aggregation,Which city has the lowest median PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city exhibited the lowest median PM10 in March 2021?,Kolar 3305,4142,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In March 2021, identify the station with the 2nd highest 25th percentile of PM2.5.","Mundka, Delhi - DPCC" 3306,4143,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 75th percentile of PM2.5 in June 2022.,Sikkim 3307,4145,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM10 in May 2021.,Mizoram 3308,4146,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In February 2021, report the state with the 2nd highest 75th percentile of PM2.5.",Uttar Pradesh 3309,4147,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state had the 2nd lowest 75th percentile of PM10 in July 2021?,Mizoram 3310,4148,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In August 2023, identify the station with the 3rd highest 25th percentile of PM2.5.","Rishi Nagar, Kaithal - HSPCB" 3311,4150,spatial_aggregation,Which city has the highest median PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In July 2019, which city registered the highest median PM10?",Jodhpur 3312,4151,spatial_aggregation,Which city has the 2nd highest median PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM2.5 in May 2021.,Manesar 3313,4152,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In April 2019, report the city with the 2nd highest 25th percentile of PM2.5.",Bhiwadi 3314,4153,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state showed the 3rd lowest median PM2.5 in March 2022?,Chhattisgarh 3315,4154,spatial_aggregation,Which station has the 3rd highest average PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In July 2023, identify the station with the 3rd highest average PM10.","LGBI Airport, Guwahati - PCBA" 3316,4155,spatial_aggregation,Which station has the 2nd highest median PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM2.5 in March 2021.,"Bawana, Delhi - DPCC" 3317,4156,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In December 2020, which station had the 3rd highest 75th percentile of PM2.5?","Talkatora District Industries Center, Lucknow - CPCB" 3318,4158,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In June 2021, report the state with the 3rd highest 25th percentile of PM10.",Rajasthan 3319,4159,spatial_aggregation,Which city has the 2nd lowest median PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city showed the 2nd lowest median PM10 in April 2020?,Aizawl 3320,4160,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In September 2022, identify the city with the 3rd lowest 25th percentile of PM2.5.",Thiruvananthapuram 3321,4161,spatial_aggregation,Which station has the 3rd highest average PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM2.5 in June 2023.,"DRCC Anandpur, Begusarai - BSPCB" 3322,4162,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In March 2024, which station recorded the 2nd highest 75th percentile of PM2.5?","Mundka, Delhi - DPCC" 3323,4163,spatial_aggregation,Which city has the highest 25th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest 25th percentile of PM10 in February 2021.,Ghaziabad 3324,4165,spatial_aggregation,Which city has the highest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city displayed the highest average PM2.5 in December 2019?,Ghaziabad 3325,4166,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In September 2019, identify the station with the lowest 75th percentile of PM10.","Pimpleshwar Mandir, Thane - MPCB" 3326,4167,spatial_aggregation,Which state has the 3rd lowest median PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM10 in May 2023.,Manipur 3327,4168,spatial_aggregation,Which city has the highest average PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In September 2021, which city exhibited the highest average PM10?",Srinagar 3328,4169,spatial_aggregation,Which station has the 2nd highest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest median PM2.5 in January 2020.,"Nehru Nagar, Delhi - DPCC" 3329,4170,spatial_aggregation,Which station has the lowest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In July 2022, report the station with the lowest median PM10.","Zero Point GICI, Gangtok - SSPCB" 3330,4171,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 25th percentile of PM2.5 in July 2024?,Aizawl 3331,4172,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2020, identify the state with the 3rd highest 25th percentile of PM10.",Delhi 3332,4173,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in January 2023.,Bihar 3333,4175,spatial_aggregation,Which station has the 2nd highest average PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM2.5 in April 2021.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 3334,4176,spatial_aggregation,Which state has the 2nd highest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In November 2023, report the state with the 2nd highest median PM10.",Haryana 3335,4178,spatial_aggregation,Which state has the 2nd highest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In May 2019, identify the state with the 2nd highest median PM2.5.",Delhi 3336,4179,spatial_aggregation,Which city has the 2nd highest average PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM10 in July 2020.,Ballabgarh 3337,4180,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In October 2022, which city recorded the 2nd lowest 75th percentile of PM2.5?",Gangtok 3338,4181,spatial_aggregation,Which station has the 3rd highest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 in August 2024.,"Alandur Bus Depot, Chennai - CPCB" 3339,4182,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In December 2022, report the city with the 3rd lowest 75th percentile of PM2.5.",Chikkamagaluru 3340,4183,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state displayed the 3rd lowest 25th percentile of PM10 in June 2023?,Mizoram 3341,4184,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In September 2020, identify the state with the lowest 75th percentile of PM10.",Mizoram 3342,4185,spatial_aggregation,Which city has the highest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest average PM10 in May 2024.,Sri Ganganagar 3343,4186,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In June 2024, which station exhibited the 3rd highest 25th percentile of PM2.5?","MD University, Rohtak - HSPCB" 3344,4187,spatial_aggregation,Which station has the 2nd highest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM2.5 in May 2023.,"Central Academy for SFS, Byrnihat - PCBA" 3345,4189,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM2.5 in May 2021?,"Loni, Ghaziabad - UPPCB" 3346,4190,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In November 2022, identify the city with the lowest 25th percentile of PM2.5.",Aizawl 3347,4191,spatial_aggregation,Which state has the 3rd highest median PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest median PM10 in October 2024.,Chandigarh 3348,4193,spatial_aggregation,Which station has the lowest average PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM2.5 in July 2019.,"Urban, Chamarajanagar - KSPCB" 3349,4194,spatial_aggregation,Which station has the 3rd lowest median PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In March 2021, report the station with the 3rd lowest median PM10.","Alandi, Pune - IITM" 3350,4195,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 75th percentile of PM10 in January 2021?,"Tamaka Ind. Area, Kolar - KSPCB" 3351,4196,spatial_aggregation,Which state has the 2nd lowest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In April 2024, identify the state with the 2nd lowest average PM10.",Jammu and Kashmir 3352,4198,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In February 2023, which state recorded the 3rd highest 75th percentile of PM2.5?",Assam 3353,4199,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM10 in May 2019.,"Tamaka Ind. Area, Kolar - KSPCB" 3354,4200,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In January 2021, report the station with the 3rd highest 75th percentile of PM2.5.","ITO, Delhi - CPCB" 3355,4201,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city displayed the 2nd lowest median PM2.5 in December 2023?,Aizawl 3356,4202,spatial_aggregation,Which station has the lowest median PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In October 2021, identify the station with the lowest median PM10.","Lumpyngngad, Shillong - Meghalaya PCB" 3357,4203,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 25th percentile of PM2.5 in May 2024.,"New Industrial Town, Faridabad - HSPCB" 3358,4204,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In September 2020, which city exhibited the 3rd highest 25th percentile of PM2.5?",Yamuna Nagar 3359,4205,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 25th percentile of PM2.5 in January 2021.,Bagalkot 3360,4207,spatial_aggregation,Which station has the 3rd highest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in November 2022?,"Chitragupta Nagar, Siwan - BSPCB" 3361,4209,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 25th percentile of PM10 in August 2023.,Delhi 3362,4210,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In December 2021, which city registered the 3rd lowest median PM2.5?",Shillong 3363,4211,spatial_aggregation,Which state has the 2nd lowest average PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM10 in December 2021.,Mizoram 3364,4213,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest 25th percentile of PM2.5 in March 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 3365,4214,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In June 2019, identify the city with the 3rd highest 75th percentile of PM10.",Ghaziabad 3366,4215,spatial_aggregation,Which station has the highest average PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest average PM10 in July 2020.,"GIDC, Nandesari - Nandesari Ind. Association" 3367,4216,spatial_aggregation,Which state has the lowest average PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In June 2022, which state recorded the lowest average PM10?",Sikkim 3368,4217,spatial_aggregation,Which city has the lowest average PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest average PM10 in July 2022.,Gangtok 3369,4218,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In November 2023, report the city with the 2nd lowest median PM2.5.",Gangtok 3370,4219,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state displayed the 2nd highest 25th percentile of PM10 in November 2023?,Haryana 3371,4220,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In September 2018, identify the city with the lowest 25th percentile of PM10.",Talcher 3372,4221,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in April 2018.,Bhiwadi 3373,4222,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In April 2023, which city exhibited the 2nd highest 25th percentile of PM2.5?",Begusarai 3374,4223,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM10 in May 2019.,Kolar 3375,4224,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In July 2023, report the station with the 3rd lowest 75th percentile of PM2.5.","Zero Point GICI, Gangtok - SSPCB" 3376,4225,spatial_aggregation,Which city has the lowest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest average PM2.5 in March 2019?,Satna 3377,4227,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM10 in June 2018.,Delhi 3378,4228,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In December 2019, which city registered the 2nd lowest 25th percentile of PM2.5?",Eloor 3379,4229,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 75th percentile of PM10 in November 2024.,Haryana 3380,4230,spatial_aggregation,Which city has the 3rd lowest average PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In August 2018, report the city with the 3rd lowest average PM10.",Siliguri 3381,4231,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest 75th percentile of PM2.5 in December 2020?,"Loni, Ghaziabad - UPPCB" 3382,4233,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM2.5 in May 2024.,Puducherry 3383,4237,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station displayed the 3rd highest 75th percentile of PM2.5 in August 2023?,"Sector-51, Gurugram - HSPCB" 3384,4240,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In March 2020, which state exhibited the 2nd highest 25th percentile of PM2.5?",Jharkhand 3385,4241,spatial_aggregation,Which state has the 2nd highest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest median PM2.5 in November 2021.,Uttar Pradesh 3386,4242,spatial_aggregation,Which state has the highest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In October 2018, report the state with the highest average PM2.5.",Delhi 3387,4243,spatial_aggregation,Which city has the lowest average PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest average PM10 in August 2021?,Udupi 3388,4244,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In November 2018, identify the station with the 3rd lowest median PM2.5.","Tamaka Ind. Area, Kolar - KSPCB" 3389,4245,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM2.5 in March 2024.,Sikkim 3390,4246,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In September 2018, which station registered the 3rd highest 25th percentile of PM2.5?","NSIT Dwarka, Delhi - CPCB" 3391,4247,spatial_aggregation,Which state has the 2nd highest average PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest average PM2.5 in August 2019.,Bihar 3392,4248,spatial_aggregation,Which state has the lowest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In October 2020, report the state with the lowest average PM2.5.",Mizoram 3393,4249,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile of PM2.5 in October 2022?,Muzaffarnagar 3394,4250,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In October 2022, identify the station with the 3rd lowest median PM2.5.","DM College of Science, Imphal - Manipur PCB" 3395,4251,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 25th percentile of PM2.5 in October 2021.,"Loni, Ghaziabad - UPPCB" 3396,4253,spatial_aggregation,Which city has the highest 75th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest 75th percentile of PM10 in June 2019.,Fatehabad 3397,4254,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In January 2018, report the city with the 2nd highest 75th percentile of PM2.5.",Kanpur 3398,4255,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city displayed the 3rd highest 25th percentile of PM10 in August 2019?,Manesar 3399,4256,spatial_aggregation,Which city has the lowest average PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In January 2022, identify the city with the lowest average PM10.",Nandesari 3400,4257,spatial_aggregation,Which state has the lowest median PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest median PM2.5 in September 2018.,Kerala 3401,4258,spatial_aggregation,Which station has the 3rd highest average PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In August 2018, which station exhibited the 3rd highest average PM2.5?","Tamaka Ind. Area, Kolar - KSPCB" 3402,4259,spatial_aggregation,Which city has the 3rd highest average PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest average PM10 in November 2022.,Katihar 3403,4260,spatial_aggregation,Which state has the lowest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In April 2019, report the state with the lowest average PM10.",Tamil Nadu 3404,4261,spatial_aggregation,Which city has the 3rd highest median PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest median PM2.5 in September 2019?,Jodhpur 3405,4262,spatial_aggregation,Which city has the 3rd highest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In December 2022, identify the city with the 3rd highest median PM2.5.",Siwan 3406,4263,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 25th percentile of PM10 in January 2024.,"Zero Point GICI, Gangtok - SSPCB" 3407,4265,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 25th percentile of PM2.5 in February 2018.,"Velachery Res. Area, Chennai - CPCB" 3408,4266,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In December 2021, report the city with the 3rd lowest 25th percentile of PM10.",Aizawl 3409,4267,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile of PM10 in October 2020?,Panipat 3410,4268,spatial_aggregation,Which station has the highest average PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In October 2023, identify the station with the highest average PM2.5.","Loni, Ghaziabad - UPPCB" 3411,4269,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 75th percentile of PM2.5 in September 2021.,Mizoram 3412,4270,spatial_aggregation,Which station has the 3rd lowest median PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In November 2019, which station recorded the 3rd lowest median PM10?","Airoli, Navi Mumbai - MPCB" 3413,4271,spatial_aggregation,Which station has the lowest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM10 in April 2022.,"Brahmagiri, Udupi - KSPCB" 3414,4272,spatial_aggregation,Which state has the highest 75th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In April 2024, report the state with the highest 75th percentile of PM10.",Delhi 3415,4273,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city displayed the 2nd lowest 25th percentile of PM10 in May 2021?,Kolar 3416,4275,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM10 in September 2024.,Meghalaya 3417,4276,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In June 2024, which state exhibited the 2nd lowest 75th percentile of PM2.5?",Sikkim 3418,4277,spatial_aggregation,Which city has the 2nd highest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM2.5 in November 2024.,Hajipur 3419,4278,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In November 2022, report the state with the 2nd lowest 25th percentile of PM2.5.",Sikkim 3420,4279,spatial_aggregation,Which state has the highest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest average PM10 in June 2018?,Uttar Pradesh 3421,4280,spatial_aggregation,Which station has the highest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In November 2020, identify the station with the highest median PM10.","Loni, Ghaziabad - UPPCB" 3422,4281,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest average PM2.5 in January 2023.,Silchar 3423,4282,spatial_aggregation,Which station has the 3rd lowest median PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In July 2018, which station registered the 3rd lowest median PM10?","Gangapur Road, Nashik - MPCB" 3424,4283,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM10 in February 2020.,Coimbatore 3425,4285,spatial_aggregation,Which city has the 3rd lowest average PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city showed the 3rd lowest average PM10 in April 2021?,Eloor 3426,4286,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In December 2018, identify the station with the 2nd lowest median PM2.5.","Gangapur Road, Nashik - MPCB" 3427,4287,spatial_aggregation,Which city has the 3rd highest average PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM10 in October 2018.,Greater Noida 3428,4288,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In March 2023, which state recorded the 2nd highest 25th percentile of PM2.5?",Delhi 3429,4289,spatial_aggregation,Which station has the highest average PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest average PM10 in September 2019.,"Sirifort, Delhi - CPCB" 3430,4290,spatial_aggregation,Which station has the lowest average PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In May 2018, report the station with the lowest average PM2.5.","Kendriya Vidyalaya, Lucknow - CPCB" 3431,4291,spatial_aggregation,Which state has the highest average PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state displayed the highest average PM10 in February 2021?,Uttar Pradesh 3432,4292,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In April 2023, identify the state with the 3rd lowest 25th percentile of PM10.",Puducherry 3433,4293,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM2.5 in March 2021.,Davanagere 3434,4294,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In August 2021, which city exhibited the 3rd lowest 25th percentile of PM2.5?",Shillong 3435,4295,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 in November 2023.,"Mundka, Delhi - DPCC" 3436,4296,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In October 2018, report the station with the 3rd lowest 25th percentile of PM10.","Plammoodu, Thiruvananthapuram - Kerala PCB" 3437,4298,spatial_aggregation,Which city has the highest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In July 2024, identify the city with the highest average PM2.5.",Byrnihat 3438,4299,spatial_aggregation,Which station has the lowest average PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest average PM10 in December 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 3439,4300,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In December 2018, which city registered the 3rd highest 75th percentile of PM10?",Delhi 3440,4301,spatial_aggregation,Which city has the 2nd highest median PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest median PM2.5 in October 2024.,Ghaziabad 3441,4303,spatial_aggregation,Which state has the 3rd lowest average PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state showed the 3rd lowest average PM10 in February 2023?,Puducherry 3442,4304,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In August 2024, identify the city with the 3rd lowest 75th percentile of PM2.5.",Imphal 3443,4305,spatial_aggregation,Which city has the highest median PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest median PM2.5 in March 2018.,Bhiwadi 3444,4307,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM2.5 in January 2020.,Eloor 3445,4308,spatial_aggregation,Which city has the 2nd highest average PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In December 2023, report the city with the 2nd highest average PM2.5.",Delhi 3446,4309,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station displayed the lowest 25th percentile of PM2.5 in April 2021?,"Velachery Res. Area, Chennai - CPCB" 3447,4310,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In February 2021, identify the station with the 2nd highest 25th percentile of PM2.5.","Bawana, Delhi - DPCC" 3448,4311,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest 25th percentile of PM2.5 in February 2024.,"Jahangirpuri, Delhi - DPCC" 3449,4312,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In December 2022, which station exhibited the 2nd lowest 75th percentile of PM2.5?","Sikulpuikawn, Aizawl - Mizoram PCB" 3450,4315,spatial_aggregation,Which state has the lowest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state had the lowest median PM10 in April 2019?,Tamil Nadu 3451,4316,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In September 2018, identify the state with the highest 75th percentile of PM2.5.",Haryana 3452,4317,spatial_aggregation,Which city has the 3rd highest average PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM2.5 in August 2021.,Jodhpur 3453,4318,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In November 2023, which state registered the 3rd lowest median PM2.5?",Puducherry 3454,4319,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 75th percentile of PM10 in November 2018.,Karnataka 3455,4320,spatial_aggregation,Which station has the lowest median PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In February 2024, report the station with the lowest median PM2.5.","Bandhavgar Colony, Satna - Birla Cement" 3456,4321,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest median PM2.5 in August 2019?,"Hardev Nagar, Bathinda - PPCB" 3457,4322,spatial_aggregation,Which state has the 3rd lowest average PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In November 2022, identify the state with the 3rd lowest average PM10.",Meghalaya 3458,4323,spatial_aggregation,Which state has the 3rd highest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest average PM2.5 in June 2024.,Delhi 3459,4324,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In December 2022, which station recorded the 3rd highest 75th percentile of PM2.5?","Kamalnath Nagar, Bettiah - BSPCB" 3460,4325,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM10 in August 2018.,"Tamaka Ind. Area, Kolar - KSPCB" 3461,4328,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In May 2024, identify the city with the 2nd lowest 25th percentile of PM2.5.",Aizawl 3462,4330,spatial_aggregation,Which state has the 3rd highest median PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2018, which state exhibited the 3rd highest median PM10?",Rajasthan 3463,4331,spatial_aggregation,Which city has the 2nd highest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM2.5 in June 2024.,Rohtak 3464,4332,spatial_aggregation,Which state has the highest median PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In July 2020, report the state with the highest median PM10.",Jharkhand 3465,4333,spatial_aggregation,Which city has the lowest median PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest median PM2.5 in September 2019?,Rajamahendravaram 3466,4334,spatial_aggregation,Which city has the 3rd highest average PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In April 2024, identify the city with the 3rd highest average PM2.5.",Muzaffarpur 3467,4336,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In December 2020, which city registered the lowest 25th percentile of PM10?",Aizawl 3468,4337,spatial_aggregation,Which city has the 3rd highest median PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM10 in December 2024.,Durgapur 3469,4340,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In December 2019, identify the state with the lowest 25th percentile of PM10.",Meghalaya 3470,4341,spatial_aggregation,Which city has the 3rd highest average PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM2.5 in September 2021.,Yamuna Nagar 3471,4342,spatial_aggregation,Which city has the lowest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In July 2019, which city recorded the lowest average PM10?",Thane 3472,4343,spatial_aggregation,Which state has the highest 25th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM10 in May 2024.,Delhi 3473,4345,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city displayed the lowest 25th percentile of PM2.5 in October 2022?,Aizawl 3474,4346,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In June 2018, identify the state with the lowest 25th percentile of PM10.",Kerala 3475,4347,spatial_aggregation,Which city has the highest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM10 in December 2022.,Begusarai 3476,4348,spatial_aggregation,Which city has the lowest median PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In April 2024, which city exhibited the lowest median PM10?",Chengalpattu 3477,4349,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM2.5 in September 2019.,Maharashtra 3478,4350,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In September 2023, report the station with the 3rd lowest average PM2.5.","Diwator Nagar, Koppal - KSPCB" 3479,4351,spatial_aggregation,Which city has the highest median PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in March 2022?,Singrauli 3480,4352,spatial_aggregation,Which state has the highest average PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In June 2019, identify the state with the highest average PM10.",Uttar Pradesh 3481,4355,spatial_aggregation,Which city has the 3rd highest median PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM10 in September 2020.,Panipat 3482,4356,spatial_aggregation,Which state has the 3rd lowest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In September 2018, report the state with the 3rd lowest median PM10.",Punjab 3483,4357,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest 75th percentile of PM2.5 in July 2023?,"Tarapur, Silchar - PCBA" 3484,4358,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In July 2021, identify the city with the 3rd highest 75th percentile of PM2.5.",Rohtak 3485,4360,spatial_aggregation,Which city has the 3rd lowest median PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In July 2020, which city recorded the 3rd lowest median PM10?",Mysuru 3486,4361,spatial_aggregation,Which state has the lowest average PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest average PM2.5 in November 2023.,Mizoram 3487,4362,spatial_aggregation,Which station has the 2nd lowest average PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In February 2018, report the station with the 2nd lowest average PM10.","Tirumala, Tirupati - APPCB" 3488,4363,spatial_aggregation,Which city has the 3rd highest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city displayed the 3rd highest average PM10 in August 2022?,Saharsa 3489,4364,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In July 2022, identify the station with the lowest 75th percentile of PM10.","Zero Point GICI, Gangtok - SSPCB" 3490,4365,spatial_aggregation,Which city has the 3rd highest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM10 in January 2023.,Katihar 3491,4367,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile of PM2.5 in June 2023.,Sikkim 3492,4368,spatial_aggregation,Which city has the 3rd highest median PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In April 2022, report the city with the 3rd highest median PM2.5.",Muzaffarnagar 3493,4369,spatial_aggregation,Which city has the highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM2.5 in July 2022?,Saharsa 3494,4370,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In September 2018, identify the station with the 2nd highest 25th percentile of PM2.5.","NISE Gwal Pahari, Gurugram - IMD" 3495,4371,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM2.5 in September 2020.,Meghalaya 3496,4372,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In November 2024, which station registered the 2nd highest 25th percentile of PM10?","Jahangirpuri, Delhi - DPCC" 3497,4375,spatial_aggregation,Which city has the 2nd highest median PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest median PM2.5 in November 2018?,Hapur 3498,4376,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In January 2019, identify the state with the 2nd lowest 75th percentile of PM10.",Karnataka 3499,4377,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM2.5 in March 2022.,Jammu and Kashmir 3500,4378,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In August 2019, which city recorded the 3rd lowest 25th percentile of PM10?",Thane 3501,4379,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 25th percentile of PM10 in July 2021.,Mizoram 3502,4380,spatial_aggregation,Which state has the 2nd lowest average PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In May 2021, report the state with the 2nd lowest average PM10.",Mizoram 3503,4381,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station displayed the 3rd highest 25th percentile of PM2.5 in November 2024?,"Anand Vihar, Delhi - DPCC" 3504,4383,spatial_aggregation,Which city has the 3rd highest average PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest average PM10 in September 2021.,Yamuna Nagar 3505,4384,spatial_aggregation,Which station has the lowest 75th percentile of PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In June 2022, which station exhibited the lowest 75th percentile of PM2.5?","Diwator Nagar, Koppal - KSPCB" 3506,4385,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM2.5 in April 2022.,Rajasthan 3507,4386,spatial_aggregation,Which city has the 3rd highest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In May 2018, report the city with the 3rd highest median PM2.5.",Jodhpur 3508,4388,spatial_aggregation,Which state has the 2nd highest median PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In February 2019, identify the state with the 2nd highest median PM2.5.",Delhi 3509,4389,spatial_aggregation,Which station has the highest median PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM10 in January 2021.,"Chandni Chowk, Delhi - IITM" 3510,4390,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In July 2022, which state registered the 3rd highest 25th percentile of PM10?",Himachal Pradesh 3511,4391,spatial_aggregation,Which city has the 2nd highest average PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest average PM2.5 in April 2024.,Gurugram 3512,4392,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In December 2023, report the state with the lowest 75th percentile of PM2.5.",Sikkim 3513,4394,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In April 2023, identify the state with the 3rd lowest 25th percentile of PM2.5.",Puducherry 3514,4395,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM10 in August 2024.,Meghalaya 3515,4397,spatial_aggregation,Which state has the 3rd highest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest median PM10 in September 2022.,Haryana 3516,4398,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In October 2022, report the state with the 2nd lowest average PM2.5.",Sikkim 3517,4401,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM2.5 in July 2019.,Satna 3518,4402,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In November 2022, which state exhibited the lowest 25th percentile of PM2.5?",Mizoram 3519,4403,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 75th percentile of PM2.5 in March 2022.,Delhi 3520,4404,spatial_aggregation,Which station has the 2nd lowest median PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In June 2021, report the station with the 2nd lowest median PM10.","Panchal Nagar, Gadag - KSPCB" 3521,4405,spatial_aggregation,Which city has the 3rd highest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest median PM10 in May 2021?,Narnaul 3522,4406,spatial_aggregation,Which station has the lowest average PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In August 2020, identify the station with the lowest average PM2.5.","Sikulpuikawn, Aizawl - Mizoram PCB" 3523,4407,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM2.5 in January 2020.,Karnataka 3524,4408,spatial_aggregation,Which state has the highest 25th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In October 2019, which state registered the highest 25th percentile of PM10?",Uttar Pradesh 3525,4409,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM2.5 in August 2022.,Chhattisgarh 3526,4410,spatial_aggregation,Which city has the 3rd lowest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In February 2024, report the city with the 3rd lowest average PM10.",Maihar 3527,4411,spatial_aggregation,Which city has the highest median PM10 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest median PM10 in October 2023?,Greater Noida 3528,4412,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In October 2018, identify the station with the 3rd lowest 75th percentile of PM2.5.","Opp GPO Civil Lines, Nagpur - MPCB" 3529,4413,spatial_aggregation,Which city has the 3rd lowest median PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM10 in March 2023.,Cuddalore 3530,4414,spatial_aggregation,Which station has the highest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In June 2022, which station recorded the highest median PM2.5?","Vikas Sadan, Gurugram - HSPCB" 3531,4415,spatial_aggregation,Which city has the lowest median PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest median PM2.5 in October 2019.,Eloor 3532,4416,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In February 2021, report the state with the 3rd lowest 25th percentile of PM2.5.",Karnataka 3533,4417,spatial_aggregation,Which station has the 2nd highest median PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station displayed the 2nd highest median PM2.5 in August 2023?,"Central Academy for SFS, Byrnihat - PCBA" 3534,4418,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In July 2018, identify the city with the 2nd lowest median PM2.5.",Tirupati 3535,4419,spatial_aggregation,Which city has the highest median PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest median PM10 in May 2020.,Nandesari 3536,4420,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In February 2020, which city exhibited the 2nd highest 75th percentile of PM2.5?",Lucknow 3537,4421,spatial_aggregation,Which station has the lowest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM2.5 in July 2018.,"Bandhavgar Colony, Satna - Birla Cement" 3538,4422,spatial_aggregation,Which city has the 2nd highest median PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In July 2021, report the city with the 2nd highest median PM10.",Rajgir 3539,4423,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest median PM2.5 in November 2022?,Kolar 3540,4424,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In November 2019, identify the state with the lowest 75th percentile of PM2.5.",Meghalaya 3541,4425,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 25th percentile of PM10 in March 2022.,"Sahilara, Maihar - KJS Cements" 3542,4427,spatial_aggregation,Which city has the 2nd lowest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest average PM10 in May 2018.,Rajamahendravaram 3543,4428,spatial_aggregation,Which city has the 2nd lowest average PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In January 2023, report the city with the 2nd lowest average PM10.",Madikeri 3544,4429,spatial_aggregation,Which state has the highest 75th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest 75th percentile of PM10 in August 2018?,Jharkhand 3545,4430,spatial_aggregation,Which station has the 3rd highest average PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In May 2018, identify the station with the 3rd highest average PM2.5.","RIICO Ind. Area III, Bhiwadi - RSPCB" 3546,4431,spatial_aggregation,Which state has the highest average PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest average PM2.5 in November 2021.,Delhi 3547,4432,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In June 2020, which state recorded the 2nd lowest average PM2.5?",Meghalaya 3548,4433,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest 25th percentile of PM2.5 in March 2021.,"Chakala-Andheri East, Mumbai - IITM" 3549,4434,spatial_aggregation,Which station has the 2nd highest average PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In October 2022, report the station with the 2nd highest average PM10.","Maharaj Bada, Gwalior - MPPCB" 3550,4436,spatial_aggregation,Which station has the highest average PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In July 2022, identify the station with the highest average PM2.5.","Vile Parle West, Mumbai - MPCB" 3551,4437,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest 75th percentile of PM10 in August 2018.,"Tamaka Ind. Area, Kolar - KSPCB" 3552,4439,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 75th percentile of PM10 in September 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 3553,4440,spatial_aggregation,Which city has the highest average PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In November 2024, report the city with the highest average PM10.",Delhi 3554,4441,spatial_aggregation,Which station has the 3rd highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM2.5 in January 2022?,"Jahangirpuri, Delhi - DPCC" 3555,4442,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In July 2020, identify the station with the 3rd lowest 25th percentile of PM10.","Hebbal 1st Stage, Mysuru - KSPCB" 3556,4445,spatial_aggregation,Which city has the highest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM2.5 in July 2020.,Ballabgarh 3557,4446,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In February 2024, report the station with the 2nd highest 75th percentile of PM2.5.","Jahangirpuri, Delhi - DPCC" 3558,4447,spatial_aggregation,Which city has the 3rd highest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest median PM10 in February 2020?,Ghaziabad 3559,4448,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In November 2023, identify the city with the 2nd highest 75th percentile of PM2.5.",Greater Noida 3560,4449,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 25th percentile of PM10 in November 2024.,Ariyalur 3561,4451,spatial_aggregation,Which city has the 3rd lowest median PM10 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest median PM10 in July 2022.,Madikeri 3562,4452,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In April 2022, report the station with the lowest 75th percentile of PM10.","Brahmagiri, Udupi - KSPCB" 3563,4454,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In August 2022, identify the state with the 3rd lowest average PM2.5.",Manipur 3564,4455,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 75th percentile of PM2.5 in February 2024.,Hapur 3565,4456,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In March 2018, which station exhibited the 2nd highest 25th percentile of PM2.5?","RIICO Ind. Area III, Bhiwadi - RSPCB" 3566,4457,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile of PM10 in December 2018.,Kerala 3567,4458,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In June 2019, report the city with the 2nd highest 75th percentile of PM2.5.",Ballabgarh 3568,4459,spatial_aggregation,Which city has the lowest average PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city had the lowest average PM10 in September 2022?,Gangtok 3569,4460,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In July 2018, identify the station with the lowest 75th percentile of PM10.","Ward-32 Bapupara, Siliguri - WBPCB" 3570,4461,spatial_aggregation,Which station has the 2nd highest average PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest average PM10 in November 2019.,"Dwarka-Sector 8, Delhi - DPCC" 3571,4462,spatial_aggregation,Which state has the highest average PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In January 2023, which state registered the highest average PM10?",Bihar 3572,4464,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In January 2019, report the state with the lowest 25th percentile of PM10.",Punjab 3573,4465,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest 75th percentile of PM2.5 in July 2020?,Aizawl 3574,4466,spatial_aggregation,Which station has the 3rd highest average PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In December 2023, identify the station with the 3rd highest average PM10.","Anand Vihar, Delhi - DPCC" 3575,4467,spatial_aggregation,Which state has the 2nd highest median PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest median PM2.5 in June 2024.,Haryana 3576,4468,spatial_aggregation,Which city has the 3rd highest average PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In March 2018, which city recorded the 3rd highest average PM10?",Talcher 3577,4469,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM2.5 in October 2021.,Delhi 3578,4470,spatial_aggregation,Which station has the lowest median PM10 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In July 2021, report the station with the lowest median PM10.","Lumpyngngad, Shillong - Meghalaya PCB" 3579,4472,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In October 2020, identify the city with the lowest 75th percentile of PM2.5.",Aizawl 3580,4473,spatial_aggregation,Which city has the 3rd lowest median PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM10 in December 2019.,Eloor 3581,4474,spatial_aggregation,Which station has the lowest median PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In November 2023, which station exhibited the lowest median PM10?","Zero Point GICI, Gangtok - SSPCB" 3582,4475,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM10 in January 2020.,Karnataka 3583,4477,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest 75th percentile of PM2.5 in June 2024?,Byrnihat 3584,4478,spatial_aggregation,Which station has the 3rd highest median PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In August 2021, identify the station with the 3rd highest median PM10.","Town Hall - Lal Bagh, Darbhanga - BSPCB" 3585,4480,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In April 2021, which city registered the 2nd highest 25th percentile of PM2.5?",Singrauli 3586,4481,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 75th percentile of PM2.5 in August 2021.,Meghalaya 3587,4482,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2021, report the state with the 3rd highest 25th percentile of PM10.",Rajasthan 3588,4483,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the 3rd lowest 75th percentile of PM10 in March 2020?,"MIDC Khutala, Chandrapur - MPCB" 3589,4484,spatial_aggregation,Which state has the highest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In June 2022, identify the state with the highest median PM2.5.",Haryana 3590,4486,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In May 2021, which state recorded the highest 75th percentile of PM2.5?",Haryana 3591,4487,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM2.5 in August 2018.,"Solapur, Solapur - MPCB" 3592,4488,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In June 2024, report the state with the lowest 25th percentile of PM2.5.",Mizoram 3593,4489,spatial_aggregation,Which state has the 3rd highest median PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state displayed the 3rd highest median PM2.5 in April 2021?,Uttar Pradesh 3594,4490,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In June 2020, identify the city with the 3rd highest 25th percentile of PM2.5.",Jodhpur 3595,4491,spatial_aggregation,Which city has the 2nd lowest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest median PM10 in September 2023.,Silchar 3596,4492,spatial_aggregation,Which station has the 2nd highest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In April 2018, which station exhibited the 2nd highest median PM2.5?","CRRI Mathura Road, Delhi - IMD" 3597,4493,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest average PM2.5 in March 2019.,Kerala 3598,4494,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In October 2019, report the station with the 2nd lowest 75th percentile of PM10.","Tamaka Ind. Area, Kolar - KSPCB" 3599,4495,spatial_aggregation,Which station has the highest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station had the highest 25th percentile of PM10 in May 2021?,"Chandni Chowk, Delhi - IITM" 3600,4496,spatial_aggregation,Which station has the 2nd highest median PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In November 2023, identify the station with the 2nd highest median PM2.5.","Bawana, Delhi - DPCC" 3601,4497,spatial_aggregation,Which city has the 3rd highest median PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM2.5 in August 2022.,Ambala 3602,4498,spatial_aggregation,Which state has the 2nd lowest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In February 2020, which state registered the 2nd lowest median PM10?",Andhra Pradesh 3603,4499,spatial_aggregation,Which station has the 3rd highest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest average PM2.5 in September 2019.,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 3604,4500,spatial_aggregation,Which station has the 2nd highest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In February 2021, report the station with the 2nd highest median PM10.","Jahangirpuri, Delhi - DPCC" 3605,4501,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest 75th percentile of PM10 in March 2022?,Maihar 3606,4502,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In February 2022, identify the station with the 3rd lowest 75th percentile of PM2.5.","Ibrahimpur, Vijayapura - KSPCB" 3607,4503,spatial_aggregation,Which state has the 2nd highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest median PM10 in February 2023.,Delhi 3608,4504,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In November 2018, which state recorded the 3rd highest 75th percentile of PM10?",Uttar Pradesh 3609,4505,spatial_aggregation,Which state has the 2nd lowest median PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest median PM10 in April 2022.,Sikkim 3610,4506,spatial_aggregation,Which city has the 2nd highest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In September 2018, report the city with the 2nd highest average PM10.",Gurugram 3611,4507,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station displayed the lowest 75th percentile of PM10 in May 2024?,"Crescent University, Chengalpattu - TNPCB" 3612,4508,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In May 2019, identify the state with the 2nd highest 75th percentile of PM10.",Delhi 3613,4509,spatial_aggregation,Which city has the 3rd lowest median PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM10 in November 2022.,Aizawl 3614,4510,spatial_aggregation,Which station has the 3rd highest median PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In May 2022, which station exhibited the 3rd highest median PM2.5?","DTU, Delhi - CPCB" 3615,4511,spatial_aggregation,Which city has the highest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM2.5 in January 2023.,Begusarai 3616,4512,spatial_aggregation,Which city has the 3rd highest median PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In October 2019, report the city with the 3rd highest median PM10.",Ghaziabad 3617,4513,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station had the 3rd lowest 75th percentile of PM2.5 in May 2022?,"DM College of Science, Imphal - Manipur PCB" 3618,4514,spatial_aggregation,Which city has the highest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In May 2021, identify the city with the highest average PM2.5.",Bhiwadi 3619,4515,spatial_aggregation,Which city has the 2nd highest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM2.5 in July 2024.,Dholpur 3620,4516,spatial_aggregation,Which station has the highest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In July 2019, which station registered the highest average PM10?","Dwarka-Sector 8, Delhi - DPCC" 3621,4517,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 75th percentile of PM2.5 in August 2018.,Tamil Nadu 3622,4518,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In September 2021, report the state with the 2nd lowest 75th percentile of PM10.",Mizoram 3623,4519,spatial_aggregation,Which station has the highest 25th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest 25th percentile of PM10 in November 2019?,"Bawana, Delhi - DPCC" 3624,4520,spatial_aggregation,Which station has the lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In June 2018, identify the station with the lowest median PM2.5.","Bandhavgar Colony, Satna - Birla Cement" 3625,4522,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In October 2018, which state recorded the 3rd lowest average PM2.5?",Andhra Pradesh 3626,4523,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 in April 2020.,"Tirumala, Tirupati - APPCB" 3627,4524,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In April 2021, report the city with the 2nd highest 25th percentile of PM10.",Singrauli 3628,4525,spatial_aggregation,Which state has the highest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state displayed the highest average PM2.5 in May 2023?,Jharkhand 3629,4526,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In November 2020, identify the station with the 3rd lowest 25th percentile of PM10.","Urban, Chamarajanagar - KSPCB" 3630,4527,spatial_aggregation,Which city has the 2nd highest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM2.5 in February 2020.,Muzaffarpur 3631,4529,spatial_aggregation,Which state has the highest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM10 in October 2021.,Delhi 3632,4530,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In December 2019, report the station with the lowest 25th percentile of PM2.5.","Lumpyngngad, Shillong - Meghalaya PCB" 3633,4531,spatial_aggregation,Which city has the 2nd highest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city had the 2nd highest median PM10 in May 2021?,Baghpat 3634,4532,spatial_aggregation,Which station has the highest 25th percentile of PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In August 2020, identify the station with the highest 25th percentile of PM10.","GIDC, Nandesari - Nandesari Ind. Association" 3635,4535,spatial_aggregation,Which station has the highest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM10 in September 2023.,"Knowledge Park - V, Greater Noida - UPPCB" 3636,4538,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In July 2018, identify the city with the 3rd lowest average PM2.5.",Chikkaballapur 3637,4539,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 25th percentile of PM2.5 in November 2024.,Aizawl 3638,4542,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In November 2020, report the state with the lowest 75th percentile of PM2.5.",Meghalaya 3639,4543,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station displayed the 3rd highest 25th percentile of PM10 in June 2019?,"Vasundhara, Ghaziabad - UPPCB" 3640,4544,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In July 2023, identify the station with the lowest 75th percentile of PM10.","Zero Point GICI, Gangtok - SSPCB" 3641,4545,spatial_aggregation,Which state has the highest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM2.5 in July 2021.,Delhi 3642,4546,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In October 2020, which state exhibited the 3rd lowest median PM2.5?",Kerala 3643,4547,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM2.5 in September 2020.,"Palayam, Kozhikode - Kerala PCB" 3644,4548,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In August 2021, report the state with the 2nd lowest 25th percentile of PM10.",Mizoram 3645,4549,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM10 in January 2020?,Mysuru 3646,4551,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 75th percentile of PM2.5 in September 2022.,"Kariavattom, Thiruvananthapuram - Kerala PCB" 3647,4552,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In October 2022, which station registered the highest 25th percentile of PM2.5?","Burari Crossing, Delhi - IMD" 3648,4553,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 in June 2024.,Tirupur 3649,4554,spatial_aggregation,Which state has the lowest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In February 2023, report the state with the lowest median PM10.",Arunachal Pradesh 3650,4555,spatial_aggregation,Which state has the highest median PM10 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest median PM10 in March 2020?,Assam 3651,4556,spatial_aggregation,Which city has the 2nd highest median PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In December 2020, identify the city with the 2nd highest median PM10.",Lucknow 3652,4557,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest median PM2.5 in March 2019.,Vijayawada 3653,4558,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In November 2019, which state recorded the 3rd highest 75th percentile of PM2.5?",Bihar 3654,4559,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM2.5 in April 2020.,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 3655,4562,spatial_aggregation,Which city has the 2nd lowest average PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In January 2021, identify the city with the 2nd lowest average PM10.",Shillong 3656,4563,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 25th percentile of PM2.5 in March 2019.,Ballabgarh 3657,4564,spatial_aggregation,Which city has the 2nd highest average PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In December 2021, which city exhibited the 2nd highest average PM10?",Bihar Sharif 3658,4565,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 75th percentile of PM10 in July 2024.,"Crescent University, Chengalpattu - TNPCB" 3659,4566,spatial_aggregation,Which station has the 3rd lowest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In May 2018, report the station with the 3rd lowest average PM10.","PWD Grounds, Vijayawada - APPCB" 3660,4567,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 25th percentile of PM2.5 in September 2021?,Jodhpur 3661,4568,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In April 2019, identify the city with the highest 25th percentile of PM2.5.",Ballabgarh 3662,4569,spatial_aggregation,Which station has the 2nd lowest median PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM10 in July 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 3663,4570,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In January 2019, which station registered the 2nd lowest 25th percentile of PM10?","Tamaka Ind. Area, Kolar - KSPCB" 3664,4572,spatial_aggregation,Which city has the lowest median PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In February 2020, report the city with the lowest median PM10.",Chamarajanagar 3665,4573,spatial_aggregation,Which city has the lowest median PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest median PM10 in July 2023?,Gangtok 3666,4574,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In February 2020, identify the city with the 3rd lowest 25th percentile of PM2.5.",Mysuru 3667,4575,spatial_aggregation,Which city has the 2nd highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM2.5 in July 2022.,Yamuna Nagar 3668,4576,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In May 2020, which station recorded the 2nd lowest 25th percentile of PM10?","Lumpyngngad, Shillong - Meghalaya PCB" 3669,4577,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 75th percentile of PM2.5 in February 2023.,Arunachal Pradesh 3670,4579,spatial_aggregation,Which station has the lowest average PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station displayed the lowest average PM2.5 in April 2018?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3671,4580,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In January 2018, identify the city with the 2nd highest 25th percentile of PM10.",Bhiwadi 3672,4581,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 75th percentile of PM2.5 in October 2018.,Haryana 3673,4582,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In July 2023, which city exhibited the 2nd highest 25th percentile of PM2.5?",Byrnihat 3674,4583,spatial_aggregation,Which state has the 3rd highest median PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest median PM2.5 in February 2021.,Assam 3675,4584,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In November 2021, report the station with the 3rd highest 25th percentile of PM10.","Wazirpur, Delhi - DPCC" 3676,4585,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 75th percentile of PM10 in December 2019?,"Anand Vihar, Delhi - DPCC" 3677,4587,spatial_aggregation,Which state has the 3rd highest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest median PM2.5 in March 2019.,Uttar Pradesh 3678,4589,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 75th percentile of PM10 in September 2024.,Meghalaya 3679,4590,spatial_aggregation,Which state has the 2nd highest median PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In May 2024, report the state with the 2nd highest median PM2.5.",Haryana 3680,4591,spatial_aggregation,Which station has the 2nd highest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest average PM2.5 in June 2024?,"Shadipur, Delhi - CPCB" 3681,4592,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In January 2021, identify the state with the 3rd lowest median PM2.5.",Mizoram 3682,4593,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 75th percentile of PM2.5 in May 2018.,Thiruvananthapuram 3683,4594,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In November 2022, which state recorded the 3rd lowest 25th percentile of PM10?",Sikkim 3684,4595,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM2.5 in December 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 3685,4596,spatial_aggregation,Which state has the 2nd lowest average PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In January 2018, report the state with the 2nd lowest average PM10.",Karnataka 3686,4597,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station displayed the lowest 25th percentile of PM10 in August 2023?,"Tarapur, Silchar - PCBA" 3687,4599,spatial_aggregation,Which city has the lowest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest median PM2.5 in August 2024.,Aizawl 3688,4600,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In December 2020, which city exhibited the lowest 75th percentile of PM2.5?",Satna 3689,4601,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest 25th percentile of PM10 in January 2023.,Himachal Pradesh 3690,4603,spatial_aggregation,Which state has the highest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in March 2019?,Assam 3691,4604,spatial_aggregation,Which station has the 2nd highest average PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In April 2022, identify the station with the 2nd highest average PM2.5.","Sector 11, Faridabad - HSPCB" 3692,4605,spatial_aggregation,Which city has the 2nd highest median PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM2.5 in December 2018.,Muzaffarpur 3693,4606,spatial_aggregation,Which city has the highest median PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In November 2019, which city registered the highest median PM10?",Panipat 3694,4607,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 25th percentile of PM2.5 in March 2023.,Puducherry 3695,4608,spatial_aggregation,Which city has the 2nd highest average PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In December 2021, report the city with the 2nd highest average PM2.5.",Siwan 3696,4609,spatial_aggregation,Which station has the 2nd lowest average PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest average PM10 in June 2022?,"Sector-19A Nerul, Navi Mumbai - IITM" 3697,4610,spatial_aggregation,Which state has the 3rd highest average PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2019, identify the state with the 3rd highest average PM10.",Delhi 3698,4612,spatial_aggregation,Which city has the highest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In March 2020, which city recorded the highest median PM2.5?",Guwahati 3699,4614,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In October 2024, report the city with the 2nd lowest 25th percentile of PM10.",Gangtok 3700,4615,spatial_aggregation,Which station has the 3rd lowest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station displayed the 3rd lowest median PM10 in February 2022?,"Lumpyngngad, Shillong - Meghalaya PCB" 3701,4616,spatial_aggregation,Which station has the highest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In May 2022, identify the station with the highest 75th percentile of PM10.","Vile Parle West, Mumbai - MPCB" 3702,4617,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 75th percentile of PM2.5 in October 2022.,Manipur 3703,4618,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In June 2018, which city exhibited the 2nd lowest median PM2.5?",Thane 3704,4619,spatial_aggregation,Which station has the 2nd highest average PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM2.5 in October 2024.,"CRRI Mathura Road, Delhi - IMD" 3705,4620,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In June 2021, report the station with the 3rd highest 25th percentile of PM10.","Nerul, Navi Mumbai - MPCB" 3706,4621,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 25th percentile of PM2.5 in August 2024?,Tirupur 3707,4622,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In April 2024, identify the station with the 3rd lowest 25th percentile of PM10.","Jawahar Nagar, Puducherry - PPCC" 3708,4623,spatial_aggregation,Which station has the 2nd lowest average PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest average PM10 in July 2020.,"Lumpyngngad, Shillong - Meghalaya PCB" 3709,4624,spatial_aggregation,Which state has the highest average PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In December 2023, which state registered the highest average PM10?",Delhi 3710,4626,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In April 2023, report the city with the 2nd lowest 25th percentile of PM2.5.",Silchar 3711,4627,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 25th percentile of PM10 in February 2020?,Delhi 3712,4628,spatial_aggregation,Which city has the 3rd highest median PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In September 2022, identify the city with the 3rd highest median PM2.5.",Kochi 3713,4629,spatial_aggregation,Which state has the 3rd lowest average PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM10 in February 2024.,Puducherry 3714,4630,spatial_aggregation,Which city has the 2nd highest average PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In September 2024, which city recorded the 2nd highest average PM10?",Sri Ganganagar 3715,4631,spatial_aggregation,Which city has the lowest average PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest average PM10 in June 2022.,Udupi 3716,4632,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In February 2023, report the station with the 2nd lowest 25th percentile of PM2.5.","Sector-3B Avas Vikas Colony, Agra - UPPCB" 3717,4633,spatial_aggregation,Which state has the 3rd highest average PM2.5 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state displayed the 3rd highest average PM2.5 in September 2018?,Uttar Pradesh 3718,4634,spatial_aggregation,Which state has the highest 25th percentile of PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In May 2021, identify the state with the highest 25th percentile of PM10.",Jharkhand 3719,4635,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 75th percentile of PM2.5 in March 2021.,Uttar Pradesh 3720,4636,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In August 2024, which state exhibited the highest 25th percentile of PM2.5?",Himachal Pradesh 3721,4637,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 25th percentile of PM10 in December 2019.,"Lumpyngngad, Shillong - Meghalaya PCB" 3722,4638,spatial_aggregation,Which city has the 3rd highest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In November 2020, report the city with the 3rd highest median PM10.",Lucknow 3723,4639,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 25th percentile of PM10 in December 2021?,"Brahmagiri, Udupi - KSPCB" 3724,4640,spatial_aggregation,Which state has the highest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In November 2020, identify the state with the highest median PM10.",Delhi 3725,4641,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 25th percentile of PM2.5 in April 2024.,Puducherry 3726,4642,spatial_aggregation,Which city has the 3rd lowest median PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In October 2018, which city registered the 3rd lowest median PM10?",Vijayawada 3727,4644,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In June 2023, report the state with the 3rd lowest median PM2.5.",Arunachal Pradesh 3728,4645,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest 75th percentile of PM2.5 in August 2021?,"Anand Vihar, Delhi - DPCC" 3729,4646,spatial_aggregation,Which station has the 3rd highest average PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In October 2024, identify the station with the 3rd highest average PM2.5.","Anand Vihar, Delhi - DPCC" 3730,4647,spatial_aggregation,Which city has the 3rd highest median PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM10 in June 2023.,Begusarai 3731,4648,spatial_aggregation,Which station has the lowest average PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In April 2020, which station recorded the lowest average PM2.5?","DRM Office Danapur, Patna - BSPCB" 3732,4649,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM10 in May 2018.,Jorapokhar 3733,4650,spatial_aggregation,Which state has the highest 25th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In April 2022, report the state with the highest 25th percentile of PM10.",Delhi 3734,4651,spatial_aggregation,Which city has the highest average PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city displayed the highest average PM10 in February 2020?,Ballabgarh 3735,4652,spatial_aggregation,Which station has the 2nd highest average PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In March 2019, identify the station with the 2nd highest average PM10.","Mundka, Delhi - DPCC" 3736,4654,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In November 2022, which city exhibited the 3rd highest 75th percentile of PM10?",Katihar 3737,4655,spatial_aggregation,Which state has the highest average PM10 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 in May 2020.,Uttar Pradesh 3738,4657,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM2.5 in January 2019?,Patiala 3739,4658,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In September 2021, identify the state with the highest 75th percentile of PM2.5.",Delhi 3740,4659,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report the city with the lowest 75th percentile of PM10 in September 2018.,Talcher 3741,4660,spatial_aggregation,Which city has the 2nd highest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In May 2024, which city registered the 2nd highest average PM10?",Greater Noida 3742,4661,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 25th percentile of PM2.5 in September 2019.,Delhi 3743,4662,spatial_aggregation,Which station has the highest 75th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In August 2019, report the station with the highest 75th percentile of PM10.","Sector-2 IMT, Manesar - HSPCB" 3744,4663,spatial_aggregation,Which state has the lowest average PM2.5 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state showed the lowest average PM2.5 in April 2024?,Jammu and Kashmir 3745,4664,spatial_aggregation,Which city has the lowest average PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In December 2024, identify the city with the lowest average PM10.",Koppal 3746,4665,spatial_aggregation,Which city has the highest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest median PM2.5 in September 2020.,Bhiwadi 3747,4668,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In January 2020, report the state with the 2nd highest 25th percentile of PM10.",Delhi 3748,4669,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city displayed the 2nd lowest 75th percentile of PM10 in February 2019?,Aurangabad 3749,4670,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In January 2018, identify the station with the 3rd highest 75th percentile of PM10.","North Campus, DU, Delhi - IMD" 3750,4672,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In October 2019, which station exhibited the lowest 25th percentile of PM10?","Tamaka Ind. Area, Kolar - KSPCB" 3751,4673,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 in August 2019.,"Tamaka Ind. Area, Kolar - KSPCB" 3752,4674,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In July 2021, report the station with the 3rd lowest average PM2.5.","Lumpyngngad, Shillong - Meghalaya PCB" 3753,4675,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state had the 2nd highest 75th percentile of PM10 in April 2020?,Assam 3754,4676,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In July 2019, identify the station with the 3rd highest 75th percentile of PM10.","F-Block, Sirsa - HSPCB" 3755,4681,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 75th percentile of PM10 in September 2018?,Kerala 3756,4682,spatial_aggregation,Which city has the highest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In March 2022, identify the city with the highest 25th percentile of PM10.",Saharsa 3757,4683,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 25th percentile of PM2.5 in May 2023.,Ramanathapuram 3758,4685,spatial_aggregation,Which station has the lowest average PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest average PM2.5 in June 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 3759,4686,spatial_aggregation,Which city has the lowest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In November 2021, report the city with the lowest median PM10.",Udupi 3760,4687,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city displayed the 3rd highest 75th percentile of PM2.5 in December 2018?,Greater Noida 3761,4688,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In October 2019, identify the state with the 2nd lowest 75th percentile of PM2.5.",Meghalaya 3762,4689,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM2.5 in December 2022.,"Deen Dayal Nagar, Sagar - MPPCB" 3763,4690,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In March 2022, which station exhibited the 2nd lowest 75th percentile of PM10?","Velachery Res. Area, Chennai - CPCB" 3764,4693,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station had the 2nd lowest 75th percentile of PM10 in November 2023?,"Zero Point GICI, Gangtok - SSPCB" 3765,4695,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in May 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 3766,4696,spatial_aggregation,Which station has the highest average PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In March 2022, which station registered the highest average PM2.5?","Loni, Ghaziabad - UPPCB" 3767,4698,spatial_aggregation,Which station has the lowest median PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In June 2022, report the station with the lowest median PM10.","Brahmagiri, Udupi - KSPCB" 3768,4700,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In January 2024, identify the state with the 2nd highest 25th percentile of PM10.",Himachal Pradesh 3769,4702,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In March 2022, which city recorded the 2nd highest 25th percentile of PM10?",Samastipur 3770,4703,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 25th percentile of PM2.5 in May 2023.,Aizawl 3771,4706,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In September 2020, identify the state with the 2nd highest 25th percentile of PM2.5.",Delhi 3772,4707,spatial_aggregation,Which station has the highest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM2.5 in January 2023.,"DRCC Anandpur, Begusarai - BSPCB" 3773,4708,spatial_aggregation,Which station has the highest median PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In September 2024, which station exhibited the highest median PM10?","GIDC, Nandesari - Nandesari Ind. Association" 3774,4709,spatial_aggregation,Which state has the highest average PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM10 in February 2019.,Odisha 3775,4710,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In March 2019, report the station with the 3rd lowest 25th percentile of PM2.5.","Hardev Nagar, Bathinda - PPCB" 3776,4711,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 75th percentile of PM2.5 in March 2021?,Delhi 3777,4712,spatial_aggregation,Which city has the 3rd lowest average PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In July 2024, identify the city with the 3rd lowest average PM10.",Gangtok 3778,4714,spatial_aggregation,Which station has the highest average PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In April 2020, which station registered the highest average PM10?","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 3779,4717,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest 75th percentile of PM2.5 in September 2024?,"Chitragupta Nagar, Siwan - BSPCB" 3780,4718,spatial_aggregation,Which city has the 3rd lowest median PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In August 2023, identify the city with the 3rd lowest median PM10.",Gangtok 3781,4719,spatial_aggregation,Which station has the highest median PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest median PM2.5 in February 2023.,"DRCC Anandpur, Begusarai - BSPCB" 3782,4721,spatial_aggregation,Which station has the 2nd highest median PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest median PM2.5 in January 2022.,"Town Hall, Munger - BSPCB" 3783,4722,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In August 2022, report the state with the 2nd lowest average PM2.5.",Sikkim 3784,4723,spatial_aggregation,Which state has the 3rd highest median PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state displayed the 3rd highest median PM10 in February 2021?,Assam 3785,4725,spatial_aggregation,Which city has the highest median PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest median PM2.5 in March 2019.,Varanasi 3786,4726,spatial_aggregation,Which state has the 2nd lowest average PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In June 2023, which state exhibited the 2nd lowest average PM10?",Arunachal Pradesh 3787,4727,spatial_aggregation,Which station has the lowest median PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest median PM2.5 in April 2023.,"Science Center, Surat - SMC" 3788,4730,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In June 2024, identify the city with the 2nd lowest 25th percentile of PM2.5.",Koppal 3789,4731,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 75th percentile of PM2.5 in December 2024.,Kerala 3790,4732,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In March 2023, which station registered the 3rd highest 25th percentile of PM10?","Muradpur, Patna - BSPCB" 3791,4733,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 25th percentile of PM2.5 in December 2022.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 3792,4734,spatial_aggregation,Which state has the 3rd highest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2024, report the state with the 3rd highest median PM10.",Telangana 3793,4735,spatial_aggregation,Which city has the highest average PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest average PM2.5 in February 2023?,Begusarai 3794,4737,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM10 in September 2021.,Chhattisgarh 3795,4738,spatial_aggregation,Which city has the 2nd highest median PM10 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In September 2022, which city recorded the 2nd highest median PM10?",Ambala 3796,4739,spatial_aggregation,Which city has the 3rd highest median PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM2.5 in November 2023.,Begusarai 3797,4741,spatial_aggregation,Which city has the 3rd highest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city displayed the 3rd highest average PM10 in April 2022?,Greater Noida 3798,4742,spatial_aggregation,Which state has the highest 75th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In January 2020, identify the state with the highest 75th percentile of PM10.",Delhi 3799,4746,spatial_aggregation,Which station has the 2nd highest average PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In March 2021, report the station with the 2nd highest average PM10.","Mundka, Delhi - DPCC" 3800,4747,spatial_aggregation,Which state has the highest median PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM2.5 in February 2020?,Assam 3801,4749,spatial_aggregation,Which station has the 3rd highest average PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM10 in April 2022.,"Suryakiran Bhawan NCL, Singrauli - MPPCB" 3802,4750,spatial_aggregation,Which city has the highest average PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In October 2024, which city registered the highest average PM2.5?",Imphal 3803,4751,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 25th percentile of PM2.5 in October 2019.,"Hombegowda Nagar, Bengaluru - KSPCB" 3804,4752,spatial_aggregation,Which station has the 3rd lowest median PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In May 2022, report the station with the 3rd lowest median PM10.","Brahmagiri, Udupi - KSPCB" 3805,4754,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In January 2023, identify the city with the 3rd lowest 25th percentile of PM2.5.",Silchar 3806,4755,spatial_aggregation,Which city has the 2nd lowest average PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest average PM2.5 in May 2024.,Aizawl 3807,4756,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In February 2024, which city recorded the 2nd lowest 25th percentile of PM10?",Ooty 3808,4758,spatial_aggregation,Which station has the 3rd highest median PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In March 2024, report the station with the 3rd highest median PM2.5.","Chandni Chowk, Delhi - IITM" 3809,4760,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In April 2019, identify the state with the 2nd highest 75th percentile of PM2.5.",Delhi 3810,4761,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 25th percentile of PM2.5 in June 2019.,Bhiwadi 3811,4762,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In December 2024, which station exhibited the 3rd lowest 25th percentile of PM10?","Kadri, Mangalore - KSPCB" 3812,4764,spatial_aggregation,Which station has the 3rd lowest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In November 2018, report the station with the 3rd lowest median PM10.","Chikkaballapur Rural, Chikkaballapur - KSPCB" 3813,4767,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 25th percentile of PM10 in August 2022.,Himachal Pradesh 3814,4769,spatial_aggregation,Which station has the highest average PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest average PM2.5 in August 2024.,"Sardar Patel Nagar, Dhanbad - JSPCB" 3815,4771,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state showed the 3rd lowest 75th percentile of PM10 in March 2021?,Kerala 3816,4772,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In July 2024, identify the state with the lowest 75th percentile of PM10.",Sikkim 3817,4773,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest 25th percentile of PM2.5 in February 2019.,Bathinda 3818,4774,spatial_aggregation,Which city has the highest median PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In June 2019, which city recorded the highest median PM10?",Fatehabad 3819,4775,spatial_aggregation,Which station has the highest median PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM2.5 in July 2022.,"Karve Road, Pune - MPCB" 3820,4777,spatial_aggregation,Which station has the highest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station displayed the highest 25th percentile of PM10 in June 2018?,"RIICO Ind. Area III, Bhiwadi - RSPCB" 3821,4778,spatial_aggregation,Which city has the lowest 25th percentile of PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In January 2023, identify the city with the lowest 25th percentile of PM2.5.",Aizawl 3822,4779,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 75th percentile of PM2.5 in September 2019.,Rajasthan 3823,4781,spatial_aggregation,Which station has the 3rd highest median PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest median PM2.5 in January 2020.,"Jahangirpuri, Delhi - DPCC" 3824,4782,spatial_aggregation,Which station has the highest average PM2.5 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In May 2022, report the station with the highest average PM2.5.","Karve Road, Pune - MPCB" 3825,4783,spatial_aggregation,Which station has the 2nd highest median PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest median PM2.5 in December 2023?,"Nehru Nagar, Delhi - DPCC" 3826,4784,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In November 2020, identify the state with the 3rd lowest average PM2.5.",Nagaland 3827,4786,spatial_aggregation,Which city has the lowest median PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In July 2021, which city registered the lowest median PM2.5?",Aizawl 3828,4788,spatial_aggregation,Which station has the highest median PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In March 2024, report the station with the highest median PM10.","Central Academy for SFS, Byrnihat - PCBA" 3829,4789,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 25th percentile of PM2.5 in August 2018?,"Bandhavgar Colony, Satna - Birla Cement" 3830,4791,spatial_aggregation,Which state has the 2nd lowest median PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest median PM10 in February 2024.,Sikkim 3831,4792,spatial_aggregation,Which city has the 3rd highest average PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In May 2023, which city recorded the 3rd highest average PM10?",Chhapra 3832,4793,spatial_aggregation,Which state has the 3rd highest average PM10 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest average PM10 in August 2021.,Rajasthan 3833,4794,spatial_aggregation,Which station has the lowest average PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In September 2019, report the station with the lowest average PM2.5.","Borivali East, Mumbai - MPCB" 3834,4795,spatial_aggregation,Which city has the highest 25th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city displayed the highest 25th percentile of PM10 in March 2021?,Ghaziabad 3835,4796,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In February 2020, identify the state with the 2nd highest 75th percentile of PM2.5.",Uttar Pradesh 3836,4797,spatial_aggregation,Which station has the 2nd highest average PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest average PM2.5 in March 2023.,"Sector-19A Nerul, Navi Mumbai - IITM" 3837,4798,spatial_aggregation,Which station has the 2nd lowest average PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In November 2018, which station exhibited the 2nd lowest average PM2.5?","MIDC Khutala, Chandrapur - MPCB" 3838,4799,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile of PM10 in January 2018.,Karnataka 3839,4800,spatial_aggregation,Which city has the 3rd highest average PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In January 2019, report the city with the 3rd highest average PM10.",Delhi 3840,4802,spatial_aggregation,Which state has the 3rd highest average PM2.5 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In February 2018, identify the state with the 3rd highest average PM2.5.",Uttar Pradesh 3841,4803,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest average PM2.5 in May 2020.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 3842,4806,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In April 2019, report the station with the 3rd highest 75th percentile of PM10.","Wazirpur, Delhi - DPCC" 3843,4807,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 25th percentile of PM10 in January 2019?,Singrauli 3844,4808,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In May 2023, identify the station with the 3rd highest 75th percentile of PM2.5.","NSIT Dwarka, Delhi - CPCB" 3845,4809,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest 75th percentile of PM10 in July 2018.,Jodhpur 3846,4810,spatial_aggregation,Which city has the 2nd highest median PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In January 2019, which city recorded the 2nd highest median PM10?",Talcher 3847,4812,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In February 2023, report the station with the 3rd lowest 25th percentile of PM2.5.","General Hospital, Mandikhera - HSPCB" 3848,4813,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state displayed the lowest 75th percentile of PM10 in November 2021?,Meghalaya 3849,4814,spatial_aggregation,Which station has the 2nd highest average PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In March 2023, identify the station with the 2nd highest average PM10.","Muradpur, Patna - BSPCB" 3850,4815,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in May 2024.,Haryana 3851,4817,spatial_aggregation,Which state has the highest average PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM2.5 in April 2023.,Jharkhand 3852,4819,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state had the 3rd highest 25th percentile of PM10 in May 2018?,Haryana 3853,4821,spatial_aggregation,Which state has the 3rd highest median PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest median PM10 in June 2018.,Haryana 3854,4822,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In February 2019, which state registered the 3rd lowest 25th percentile of PM10?",Andhra Pradesh 3855,4823,spatial_aggregation,Which city has the highest median PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest median PM2.5 in July 2019.,Jodhpur 3856,4824,spatial_aggregation,Which city has the 2nd highest median PM10 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In May 2023, report the city with the 2nd highest median PM10.",Sri Ganganagar 3857,4825,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile of PM10 in October 2018?,Bhiwadi 3858,4826,spatial_aggregation,Which state has the 3rd highest median PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In March 2019, identify the state with the 3rd highest median PM10.",Delhi 3859,4827,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in December 2019.,Uttar Pradesh 3860,4829,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM2.5 in July 2024.,Aizawl 3861,4830,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In January 2023, report the station with the 3rd lowest 75th percentile of PM10.","Rajbagh, Srinagar - JKSPCB" 3862,4831,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station displayed the 3rd lowest 75th percentile of PM2.5 in March 2022?,"Ibrahimpur, Vijayapura - KSPCB" 3863,4832,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In February 2019, identify the state with the 2nd lowest 25th percentile of PM10.",Punjab 3864,4833,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest 75th percentile of PM2.5 in December 2021.,Delhi 3865,4834,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In November 2020, which station exhibited the lowest 75th percentile of PM10?","Lumpyngngad, Shillong - Meghalaya PCB" 3866,4836,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In October 2018, report the station with the 3rd lowest 75th percentile of PM10.","PWD Grounds, Vijayawada - APPCB" 3867,4837,spatial_aggregation,Which station has the 3rd highest median PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest median PM10 in July 2018?,"Collectorate, Jodhpur - RSPCB" 3868,4841,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest 25th percentile of PM10 in April 2018.,Andhra Pradesh 3869,4842,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In May 2018, report the state with the lowest 25th percentile of PM2.5.",Kerala 3870,4843,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest average PM2.5 in June 2021?,Arunachal Pradesh 3871,4844,spatial_aggregation,Which state has the 3rd highest median PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In October 2022, identify the state with the 3rd highest median PM2.5.",Uttar Pradesh 3872,4845,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 25th percentile of PM2.5 in November 2019.,Muzaffarpur 3873,4846,spatial_aggregation,Which state has the 2nd highest average PM2.5 in May 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In May 2020, which state recorded the 2nd highest average PM2.5?",Uttar Pradesh 3874,4847,spatial_aggregation,Which state has the highest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest median PM2.5 in September 2020.,Uttar Pradesh 3875,4848,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In October 2020, report the station with the highest 25th percentile of PM2.5.","RIICO Ind. Area III, Bhiwadi - RSPCB" 3876,4849,spatial_aggregation,Which city has the lowest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city displayed the lowest average PM10 in May 2018?,Thiruvananthapuram 3877,4850,spatial_aggregation,Which state has the 2nd highest average PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In March 2018, identify the state with the 2nd highest average PM10.",Uttar Pradesh 3878,4851,spatial_aggregation,Which state has the highest median PM2.5 in August 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM2.5 in August 2021.,Delhi 3879,4852,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In July 2020, which station exhibited the 2nd highest 25th percentile of PM10?","GIDC, Nandesari - Nandesari Ind. Association" 3880,4854,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In July 2019, report the city with the 3rd highest 25th percentile of PM2.5.",Yamuna Nagar 3881,4855,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest 75th percentile of PM10 in February 2020?,Tirupati 3882,4856,spatial_aggregation,Which station has the highest average PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In July 2020, identify the station with the highest average PM2.5.","RIMT University, Mandi Gobindgarh - PPCB" 3883,4858,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In June 2021, which city registered the 2nd lowest 25th percentile of PM10?",Gadag 3884,4860,spatial_aggregation,Which city has the highest 75th percentile of PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In February 2021, report the city with the highest 75th percentile of PM10.",Ghaziabad 3885,4862,spatial_aggregation,Which city has the 3rd highest average PM2.5 in July 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In July 2024, identify the city with the 3rd highest average PM2.5.",Nandesari 3886,4863,spatial_aggregation,Which station has the lowest median PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest median PM2.5 in February 2019.,"Bandhavgar Colony, Satna - Birla Cement" 3887,4864,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In July 2021, which state recorded the lowest 25th percentile of PM2.5?",Mizoram 3888,4865,spatial_aggregation,Which state has the lowest median PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest median PM10 in September 2023.,Sikkim 3889,4867,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city displayed the 2nd highest 75th percentile of PM10 in May 2022?,Bihar Sharif 3890,4869,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report the state with the 3rd highest 25th percentile of PM2.5 in October 2023.,Jharkhand 3891,4870,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In September 2019, which city exhibited the 2nd lowest 25th percentile of PM2.5?",Damoh 3892,4871,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM10 in December 2022.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 3893,4872,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In August 2023, report the city with the 3rd lowest 25th percentile of PM2.5.",Aizawl 3894,4873,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM10 in March 2019?,Kerala 3895,4875,spatial_aggregation,Which city has the highest average PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest average PM10 in December 2018.,Noida 3896,4876,spatial_aggregation,Which state has the 2nd highest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In August 2022, which state registered the 2nd highest average PM10?",Himachal Pradesh 3897,4877,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest 75th percentile of PM2.5 in August 2020.,Hubballi 3898,4878,spatial_aggregation,Which state has the 3rd highest median PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In April 2018, report the state with the 3rd highest median PM10.",Haryana 3899,4879,spatial_aggregation,Which station has the highest average PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest average PM2.5 in August 2020?,"GIDC, Nandesari - Nandesari Ind. Association" 3900,4880,spatial_aggregation,Which state has the 3rd lowest average PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In April 2023, identify the state with the 3rd lowest average PM10.",Puducherry 3901,4881,spatial_aggregation,Which city has the 3rd highest median PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest median PM10 in May 2019.,Ballabgarh 3902,4882,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In November 2019, which city recorded the lowest 75th percentile of PM10?",Shillong 3903,4883,spatial_aggregation,Which state has the 3rd highest median PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest median PM10 in December 2021.,Haryana 3904,4884,spatial_aggregation,Which state has the 3rd highest median PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In July 2018, report the state with the 3rd highest median PM2.5.",Gujarat 3905,4885,spatial_aggregation,Which city has the 2nd highest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city displayed the 2nd highest median PM10 in January 2023?,Sirohi 3906,4886,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In May 2018, identify the state with the 2nd lowest median PM2.5.",Andhra Pradesh 3907,4887,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest average PM2.5 in June 2022.,"DM College of Science, Imphal - Manipur PCB" 3908,4888,spatial_aggregation,Which station has the 3rd highest average PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In November 2023, which station exhibited the 3rd highest average PM10?","Wazirpur, Delhi - DPCC" 3909,4889,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 25th percentile of PM2.5 in August 2024.,Mizoram 3910,4891,spatial_aggregation,Which city has the 3rd highest average PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in May 2018?,Ghaziabad 3911,4892,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In January 2022, identify the city with the 3rd lowest 25th percentile of PM2.5.",Chamarajanagar 3912,4894,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In July 2020, which city registered the lowest 75th percentile of PM10?",Aizawl 3913,4895,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM10 in September 2023.,Silchar 3914,4896,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In May 2022, report the station with the 2nd lowest 25th percentile of PM10.","Brahmagiri, Udupi - KSPCB" 3915,4897,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 25th percentile of PM10 in February 2024?,Jammu and Kashmir 3916,4898,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM10 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In February 2019, identify the station with the 3rd lowest 25th percentile of PM10.","Tamaka Ind. Area, Kolar - KSPCB" 3917,4899,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 25th percentile of PM2.5 in January 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 3918,4900,spatial_aggregation,Which station has the 2nd lowest median PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In January 2023, which station recorded the 2nd lowest median PM10?","Stuart Hill, Madikeri - KSPCB" 3919,4901,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM10 in April 2022.,Puducherry 3920,4902,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In December 2022, report the station with the 3rd lowest average PM2.5.","Kalyana Nagara, Chikkamagaluru - KSPCB" 3921,4903,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state displayed the 3rd lowest 25th percentile of PM2.5 in April 2020?,Andhra Pradesh 3922,4904,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In January 2022, identify the city with the lowest 75th percentile of PM10.",Nandesari 3923,4905,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 75th percentile of PM10 in January 2023.,Puducherry 3924,4906,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In May 2024, which station exhibited the 2nd highest 75th percentile of PM10?","Knowledge Park - V, Greater Noida - UPPCB" 3925,4907,spatial_aggregation,Which station has the 2nd highest average PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest average PM10 in July 2019.,"Wazirpur, Delhi - DPCC" 3926,4909,spatial_aggregation,Which city has the highest median PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest median PM10 in September 2020?,Bhiwadi 3927,4910,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In April 2021, identify the station with the highest 75th percentile of PM2.5.","Suryakiran Bhawan NCL, Singrauli - MPPCB" 3928,4911,spatial_aggregation,Which city has the 2nd highest median PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest median PM10 in April 2018.,Pune 3929,4913,spatial_aggregation,Which state has the lowest 75th percentile of PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state with the lowest 75th percentile of PM2.5 in June 2024.,Mizoram 3930,4914,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In June 2024, report the station with the 3rd lowest average PM2.5.","Plammoodu, Thiruvananthapuram - Kerala PCB" 3931,4915,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city showed the 2nd lowest 25th percentile of PM10 in January 2023?,Udupi 3932,4916,spatial_aggregation,Which city has the lowest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In March 2022, identify the city with the lowest median PM2.5.",Maihar 3933,4917,spatial_aggregation,Which state has the highest median PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM10 in November 2018.,Jharkhand 3934,4918,spatial_aggregation,Which city has the 3rd highest median PM2.5 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In January 2019, which city recorded the 3rd highest median PM2.5?",Muzaffarpur 3935,4919,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 25th percentile of PM2.5 in October 2024.,Muzaffarnagar 3936,4921,spatial_aggregation,Which station has the highest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station displayed the highest 25th percentile of PM2.5 in August 2022?,"Karve Road, Pune - MPCB" 3937,4922,spatial_aggregation,Which city has the highest 25th percentile of PM10 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In April 2023, identify the city with the highest 25th percentile of PM10.",Begusarai 3938,4923,spatial_aggregation,Which state has the 3rd lowest average PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM10 in November 2023.,Manipur 3939,4924,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In October 2024, which city exhibited the 3rd lowest median PM2.5?",Kalaburagi 3940,4926,spatial_aggregation,Which state has the 2nd highest median PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In March 2022, report the state with the 2nd highest median PM2.5.",Tripura 3941,4928,spatial_aggregation,Which station has the lowest median PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In April 2019, identify the station with the lowest median PM10.","Manali Village, Chennai - TNPCB" 3942,4930,spatial_aggregation,Which city has the lowest median PM2.5 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In June 2018, which city registered the lowest median PM2.5?",Satna 3943,4932,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In May 2023, report the station with the lowest 25th percentile of PM2.5.","Zero Point GICI, Gangtok - SSPCB" 3944,4934,spatial_aggregation,Which station has the 2nd lowest median PM10 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In October 2022, identify the station with the 2nd lowest median PM10.","Brahmagiri, Udupi - KSPCB" 3945,4935,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in November 2021.,"Panchal Nagar, Gadag - KSPCB" 3946,4936,spatial_aggregation,Which state has the 2nd highest average PM10 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In December 2023, which state recorded the 2nd highest average PM10?",Himachal Pradesh 3947,4937,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city with the 3rd lowest median PM2.5 in June 2019.,Eloor 3948,4938,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In May 2018, report the city with the highest 75th percentile of PM2.5.",Jodhpur 3949,4940,spatial_aggregation,Which city has the 3rd lowest average PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In August 2024, identify the city with the 3rd lowest average PM10.",Shillong 3950,4941,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM10 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 75th percentile of PM10 in March 2021.,Meghalaya 3951,4943,spatial_aggregation,Which state has the 2nd lowest median PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state with the 2nd lowest median PM10 in October 2020.,Meghalaya 3952,4944,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In December 2022, report the city with the 2nd highest 75th percentile of PM10.",Darbhanga 3953,4945,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM10 in February 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city had the 2nd lowest 75th percentile of PM10 in February 2024?,Vijayapura 3954,4946,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In March 2023, identify the city with the 2nd highest 75th percentile of PM2.5.",Begusarai 3955,4947,spatial_aggregation,Which state has the 2nd highest median PM10 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest median PM10 in November 2024.,Haryana 3956,4948,spatial_aggregation,Which city has the highest average PM10 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In August 2022, which city registered the highest average PM10?",Jorapokhar 3957,4951,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 75th percentile of PM2.5 in August 2022?,Sikkim 3958,4952,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In December 2019, identify the city with the 2nd highest 25th percentile of PM10.",Ballabgarh 3959,4953,spatial_aggregation,Which city has the 2nd lowest average PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report the city with the 2nd lowest average PM10 in April 2021.,Shillong 3960,4954,spatial_aggregation,Which state has the 2nd lowest median PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In December 2018, which state recorded the 2nd lowest median PM10?",Karnataka 3961,4956,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In March 2020, report the city with the highest 75th percentile of PM2.5.",Charkhi Dadri 3962,4957,spatial_aggregation,Which city has the lowest 25th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city displayed the lowest 25th percentile of PM10 in February 2020?,Chamarajanagar 3963,4958,spatial_aggregation,Which state has the 2nd lowest median PM10 in February 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In February 2018, identify the state with the 2nd lowest median PM10.",Kerala 3964,4959,spatial_aggregation,Which station has the 2nd highest median PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report the station with the 2nd highest median PM2.5 in September 2020.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 3965,4960,spatial_aggregation,Which state has the 2nd highest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In November 2024, which state exhibited the 2nd highest average PM2.5?",Chandigarh 3966,4961,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest median PM2.5 in June 2024.,"Diwator Nagar, Koppal - KSPCB" 3967,4962,spatial_aggregation,Which station has the highest median PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In April 2020, report the station with the highest median PM10.","Manali Village, Chennai - TNPCB" 3968,4963,spatial_aggregation,Which state has the highest median PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest median PM10 in December 2020?,Uttar Pradesh 3969,4964,spatial_aggregation,Which station has the 3rd highest average PM10 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In February 2021, identify the station with the 3rd highest average PM10.","Jahangirpuri, Delhi - DPCC" 3970,4965,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM2.5 in October 2018.,Andhra Pradesh 3971,4966,spatial_aggregation,Which city has the 3rd lowest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In May 2024, which city registered the 3rd lowest median PM10?",Gangtok 3972,4967,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM10 in July 2023.,Gangtok 3973,4968,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM2.5 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In March 2019, report the city with the 2nd highest 25th percentile of PM2.5.",Bhiwadi 3974,4969,spatial_aggregation,Which station has the 3rd highest median PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest median PM2.5 in June 2022?,"MD University, Rohtak - HSPCB" 3975,4971,spatial_aggregation,Which station has the highest 75th percentile of PM10 in May 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 75th percentile of PM10 in May 2018.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 3976,4972,spatial_aggregation,Which station has the lowest average PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In May 2022, which station recorded the lowest average PM10?","ECIL Kapra, Hyderabad - TSPCB" 3977,4973,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 in September 2020.,Aizawl 3978,4975,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city displayed the 3rd lowest 75th percentile of PM10 in June 2020?,Pune 3979,4977,spatial_aggregation,Which city has the 3rd lowest average PM10 in April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest average PM10 in April 2019.,Vijayawada 3980,4978,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In April 2021, which state exhibited the 3rd lowest 25th percentile of PM2.5?",Mizoram 3981,4980,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In January 2018, report the city with the 2nd lowest 75th percentile of PM2.5.",Bengaluru 3982,4983,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 25th percentile of PM10 in June 2018.,Telangana 3983,4984,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in September 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","In September 2024, which state registered the 3rd lowest 25th percentile of PM10?",Manipur 3984,4987,spatial_aggregation,Which station has the highest average PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest average PM10 in March 2018?,"DTU, Delhi - CPCB" 3985,4988,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In June 2019, identify the station with the lowest 75th percentile of PM10.","Udyogamandal, Eloor - Kerala PCB" 3986,4989,spatial_aggregation,Which state has the 2nd lowest 75th percentile of PM2.5 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest 75th percentile of PM2.5 in December 2022.,Arunachal Pradesh 3987,4990,spatial_aggregation,Which city has the 2nd lowest 25th percentile of PM2.5 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In January 2021, which city recorded the 2nd lowest 25th percentile of PM2.5?",Davanagere 3988,4991,spatial_aggregation,Which station has the highest median PM10 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station with the highest median PM10 in May 2021.,"Murthal, Sonipat - HSPCB" 3989,4992,spatial_aggregation,Which city has the highest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In January 2019, report the city with the highest 75th percentile of PM10.",Bahadurgarh 3990,4993,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station displayed the 2nd highest 75th percentile of PM2.5 in November 2018?,"Wazirpur, Delhi - DPCC" 3991,4994,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In January 2023, identify the station with the 3rd highest 75th percentile of PM10.","DRCC Anandpur, Begusarai - BSPCB" 3992,4995,spatial_aggregation,Which state has the lowest average PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest average PM10 in January 2020.,Meghalaya 3993,4997,spatial_aggregation,Which station has the 3rd lowest median PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest median PM2.5 in February 2023.,"Chalai Bazaar, Ramanathapuram - TNPCB" 3994,4998,spatial_aggregation,Which city has the highest median PM10 in December 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In December 2022, report the city with the highest median PM10.",Darbhanga 3995,5000,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM10 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In May 2019, identify the station with the 3rd highest 75th percentile of PM10.","Mundka, Delhi - DPCC" 3996,5001,spatial_aggregation,Which state has the 2nd highest average PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM10 in April 2018.,Delhi 3997,5002,spatial_aggregation,Which station has the 3rd highest average PM10 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In September 2023, which station registered the 3rd highest average PM10?","Science Center, Surat - SMC" 3998,5003,spatial_aggregation,Which city has the 3rd highest median PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM10 in April 2021.,Singrauli 3999,5005,spatial_aggregation,Which station has the highest 25th percentile of PM10 in March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the highest 25th percentile of PM10 in March 2019?,"Mundka, Delhi - DPCC" 4000,5006,spatial_aggregation,Which station has the lowest average PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In September 2018, identify the station with the lowest average PM10.","Talcher Coalfields,Talcher - OSPCB" 4001,5009,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest median PM2.5 in June 2023.,"Tarapur, Silchar - PCBA" 4002,5010,spatial_aggregation,Which state has the 2nd highest median PM10 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In April 2022, report the state with the 2nd highest median PM10.",Haryana 4003,5012,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","In February 2023, identify the city with the lowest 75th percentile of PM2.5.",Maihar 4004,5014,spatial_aggregation,Which city has the highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In February 2023, which city exhibited the highest median PM10?",Hanumangarh 4005,5015,spatial_aggregation,Which state has the highest average PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest average PM2.5 in January 2023.,Bihar 4006,5016,spatial_aggregation,Which city has the 3rd highest median PM2.5 in August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In August 2019, report the city with the 3rd highest median PM2.5.",Bhiwadi 4007,5017,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city had the highest 75th percentile of PM2.5 in November 2024?,Delhi 4008,5018,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In October 2023, identify the city with the 3rd lowest median PM2.5.",Gangtok 4009,5019,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM2.5 in February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM2.5 in February 2019.,Maihar 4010,5020,spatial_aggregation,Which station has the 2nd highest median PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In February 2021, which station registered the 2nd highest median PM2.5?","Jahangirpuri, Delhi - DPCC" 4011,5021,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM2.5 in October 2020.,Delhi 4012,5022,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in August 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In August 2018, report the city with the 2nd highest 75th percentile of PM10.",Jodhpur 4013,5023,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 75th percentile of PM10 in June 2023?,"Tarapur, Silchar - PCBA" 4014,5025,spatial_aggregation,Which city has the 3rd highest 75th percentile of PM10 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report the city with the 3rd highest 75th percentile of PM10 in June 2020.,Nandesari 4015,5026,spatial_aggregation,Which state has the highest 25th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In March 2018, which state recorded the highest 25th percentile of PM10?",Odisha 4016,5028,spatial_aggregation,Which city has the 3rd lowest average PM2.5 in June 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In June 2020, report the city with the 3rd lowest average PM2.5.",Hubballi 4017,5031,spatial_aggregation,Which state has the highest median PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report the state with the highest median PM10 in September 2018.,Haryana 4018,5032,spatial_aggregation,Which state has the 2nd lowest median PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","In February 2022, which state exhibited the 2nd lowest median PM10?",Chhattisgarh 4019,5033,spatial_aggregation,Which station has the 3rd lowest average PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM10 in October 2024.,"Bharathidasan University, Palkalaiperur - TNPCB" 4020,5035,spatial_aggregation,Which station has the 3rd highest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station had the 3rd highest 25th percentile of PM2.5 in July 2023?,"Central Academy for SFS, Byrnihat - PCBA" 4021,5036,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM10 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In June 2021, identify the state with the 2nd highest 25th percentile of PM10.",Haryana 4022,5038,spatial_aggregation,Which station has the lowest median PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","In October 2019, which station registered the lowest median PM10?","Udyogamandal, Eloor - Kerala PCB" 4023,5039,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in October 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest 75th percentile of PM2.5 in October 2022.,"Sector-51, Gurugram - HSPCB" 4024,5040,spatial_aggregation,Which state has the 3rd highest median PM10 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In February 2023, report the state with the 3rd highest median PM10.",Assam 4025,5041,spatial_aggregation,Which state has the 3rd highest 75th percentile of PM2.5 in July 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state showed the 3rd highest 75th percentile of PM2.5 in July 2021?,Gujarat 4026,5042,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In January 2023, identify the city with the 2nd highest 25th percentile of PM10.",Byrnihat 4027,5043,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 25th percentile of PM2.5 in July 2018.,Gujarat 4028,5044,spatial_aggregation,Which state has the 2nd lowest 25th percentile of PM2.5 in September 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In September 2022, which state recorded the 2nd lowest 25th percentile of PM2.5?",Sikkim 4029,5045,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in December 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest 75th percentile of PM2.5 in December 2024.,Chikkamagaluru 4030,5046,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In March 2018, report the state with the highest 25th percentile of PM2.5.",Odisha 4031,5047,spatial_aggregation,Which station has the 3rd lowest 25th percentile of PM2.5 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station displayed the 3rd lowest 25th percentile of PM2.5 in December 2018?,"MIDC Khutala, Chandrapur - MPCB" 4032,5048,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In July 2020, identify the state with the 2nd lowest median PM2.5.",Meghalaya 4033,5049,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report the city with the highest 75th percentile of PM2.5 in January 2020.,Muzaffarpur 4034,5050,spatial_aggregation,Which city has the 3rd lowest average PM10 in November 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In November 2018, which city exhibited the 3rd lowest average PM10?",Vijayawada 4035,5051,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in October 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest 75th percentile of PM2.5 in October 2023.,Arunachal Pradesh 4036,5052,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM2.5 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ","In May 2024, report the city with the 3rd lowest 25th percentile of PM2.5.",Varanasi 4037,5053,spatial_aggregation,Which state has the highest 75th percentile of PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state had the highest 75th percentile of PM2.5 in March 2024?,Assam 4038,5054,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In January 2018, identify the city with the 3rd highest 25th percentile of PM2.5.",Lucknow 4039,5056,spatial_aggregation,Which state has the 3rd highest median PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In October 2021, which state registered the 3rd highest median PM10?",Haryana 4040,5057,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station with the 2nd highest 75th percentile of PM10 in October 2020.,"Knowledge Park - V, Greater Noida - UPPCB" 4041,5058,spatial_aggregation,Which state has the 3rd highest median PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In November 2024, report the state with the 3rd highest median PM2.5.",Haryana 4042,5059,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM2.5 in August 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest 25th percentile of PM2.5 in August 2022?,"Patti Mehar, Ambala - HSPCB" 4043,5060,spatial_aggregation,Which city has the 3rd lowest median PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In January 2019, identify the city with the 3rd lowest median PM10.",Amritsar 4044,5061,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report the state with the 2nd lowest average PM2.5 in February 2020.,Tamil Nadu 4045,5062,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","In November 2023, which station recorded the 3rd lowest 75th percentile of PM2.5?","Girls College, Sivasagar - PCBA" 4046,5063,spatial_aggregation,Which station has the 3rd lowest average PM2.5 in May 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest average PM2.5 in May 2023.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 4047,5064,spatial_aggregation,Which state has the highest 25th percentile of PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In March 2023, report the state with the highest 25th percentile of PM10.",Bihar 4048,5065,spatial_aggregation,Which station has the 2nd highest median PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station displayed the 2nd highest median PM2.5 in April 2020?,"ITO, Delhi - CPCB" 4049,5066,spatial_aggregation,Which station has the lowest median PM2.5 in March 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In March 2020, identify the station with the lowest median PM2.5.","Udyogamandal, Eloor - Kerala PCB" 4050,5067,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 25th percentile of PM10 in April 2021.,Jammu and Kashmir 4051,5068,spatial_aggregation,Which city has the 2nd lowest 75th percentile of PM2.5 in April 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In April 2023, which city exhibited the 2nd lowest 75th percentile of PM2.5?",Kunjemura 4052,5070,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","In April 2020, report the station with the 2nd lowest 75th percentile of PM10.","Anand Kala Kshetram, Rajamahendravaram - APPCB" 4053,5071,spatial_aggregation,Which station has the lowest median PM10 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station had the lowest median PM10 in July 2018?,"Ward-32 Bapupara, Siliguri - WBPCB" 4054,5073,spatial_aggregation,Which city has the 3rd lowest 75th percentile of PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 75th percentile of PM10 in December 2018.,Nashik 4055,5074,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In July 2019, which state registered the lowest 25th percentile of PM10?",Kerala 4056,5076,spatial_aggregation,Which city has the highest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","In June 2018, report the city with the highest average PM10.",Bhiwadi 4057,5077,spatial_aggregation,Which city has the lowest average PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest average PM10 in January 2019?,Kolar 4058,5078,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in March 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In March 2022, identify the station with the lowest 25th percentile of PM2.5.","Sikulpuikawn, Aizawl - Mizoram PCB" 4059,5082,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in July 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In July 2020, report the state with the lowest 25th percentile of PM2.5.",Mizoram 4060,5083,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state displayed the 3rd lowest median PM2.5 in May 2019?,Andhra Pradesh 4061,5084,spatial_aggregation,Which state has the 3rd highest median PM10 in August 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In August 2023, identify the state with the 3rd highest median PM10.",Rajasthan 4062,5086,spatial_aggregation,Which city has the 3rd lowest average PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","In October 2024, which city exhibited the 3rd lowest average PM10?",Ramanathapuram 4063,5088,spatial_aggregation,Which city has the highest 75th percentile of PM2.5 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In November 2023, report the city with the highest 75th percentile of PM2.5.",Delhi 4064,5089,spatial_aggregation,Which station has the 2nd highest average PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station had the 2nd highest average PM10 in November 2023?,"Anand Vihar, Delhi - DPCC" 4065,5090,spatial_aggregation,Which station has the lowest 25th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","In August 2020, identify the station with the lowest 25th percentile of PM2.5.","Sanathnagar, Hyderabad - TSPCB" 4066,5091,spatial_aggregation,Which station has the highest 75th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report the station with the highest 75th percentile of PM2.5 in January 2024.,"Nehru Nagar, Delhi - DPCC" 4067,5092,spatial_aggregation,Which state has the 3rd highest median PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In May 2024, which state registered the 3rd highest median PM10?",Chandigarh 4068,5093,spatial_aggregation,Which station has the lowest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station with the lowest 25th percentile of PM10 in February 2022.,"GIDC, Nandesari - Nandesari Ind. Association" 4069,5094,spatial_aggregation,Which state has the 3rd highest median PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In October 2019, report the state with the 3rd highest median PM10.",Haryana 4070,5095,spatial_aggregation,Which state has the highest median PM10 in January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest median PM10 in January 2018?,Uttar Pradesh 4071,5096,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In November 2024, identify the city with the 3rd highest 25th percentile of PM2.5.",Byrnihat 4072,5097,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest 75th percentile of PM2.5 in December 2020.,Karnataka 4073,5099,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state with the 3rd lowest average PM2.5 in March 2018.,Andhra Pradesh 4074,5100,spatial_aggregation,Which city has the 3rd highest average PM2.5 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In October 2024, report the city with the 3rd highest average PM2.5.",Ghaziabad 4075,5101,spatial_aggregation,Which state has the 3rd highest 25th percentile of PM10 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state displayed the 3rd highest 25th percentile of PM10 in February 2022?,Tripura 4076,5103,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 75th percentile of PM10 in February 2020.,Uttar Pradesh 4077,5104,spatial_aggregation,Which city has the 2nd highest 25th percentile of PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In December 2020, which city exhibited the 2nd highest 25th percentile of PM10?",Bulandshahr 4078,5107,spatial_aggregation,Which city has the 3rd lowest median PM10 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city had the 3rd lowest median PM10 in November 2020?,Kolar 4079,5108,spatial_aggregation,Which station has the 3rd highest 75th percentile of PM2.5 in November 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","In November 2022, identify the station with the 3rd highest 75th percentile of PM2.5.","DRCC Anandpur, Begusarai - BSPCB" 4080,5109,spatial_aggregation,Which state has the 3rd lowest average PM2.5 in November 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest average PM2.5 in November 2024.,Sikkim 4081,5110,spatial_aggregation,Which city has the 3rd highest median PM2.5 in February 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In February 2023, which city registered the 3rd highest median PM2.5?",Saharsa 4082,5111,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city with the 2nd highest 75th percentile of PM2.5 in December 2019.,Noida 4083,5112,spatial_aggregation,Which station has the 2nd highest 25th percentile of PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In January 2020, report the station with the 2nd highest 25th percentile of PM10.","Nathu Colony, Ballabgarh - HSPCB" 4084,5113,spatial_aggregation,Which city has the 2nd highest average PM2.5 in September 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest average PM2.5 in September 2023?,Surat 4085,5114,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ","In January 2024, identify the state with the lowest 25th percentile of PM2.5.",Sikkim 4086,5115,spatial_aggregation,Which state has the lowest 25th percentile of PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report the state with the lowest 25th percentile of PM2.5 in October 2018.,Kerala 4087,5116,spatial_aggregation,Which city has the 2nd lowest median PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In January 2020, which city recorded the 2nd lowest median PM10?",Maihar 4088,5117,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in April 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state with the 2nd highest 25th percentile of PM2.5 in April 2020.,Uttar Pradesh 4089,5119,spatial_aggregation,Which state has the lowest 25th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state displayed the lowest 25th percentile of PM10 in April 2018?,Kerala 4090,5120,spatial_aggregation,Which city has the 2nd highest 75th percentile of PM10 in September 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ","In September 2021, identify the city with the 2nd highest 75th percentile of PM10.",Yamuna Nagar 4091,5121,spatial_aggregation,Which station has the 3rd highest average PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report the station with the 3rd highest average PM10 in October 2019.,"Anand Vihar, Delhi - DPCC" 4092,5122,spatial_aggregation,Which state has the 2nd highest average PM10 in May 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","In May 2024, which state exhibited the 2nd highest average PM10?",Himachal Pradesh 4093,5123,spatial_aggregation,Which station has the 3rd highest average PM10 in September 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station with the 3rd highest average PM10 in September 2020.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 4094,5124,spatial_aggregation,Which station has the 3rd highest average PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In October 2021, report the station with the 3rd highest average PM10.","Chandni Chowk, Delhi - IITM" 4095,5125,spatial_aggregation,Which state has the 3rd lowest 25th percentile of PM10 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state had the 3rd lowest 25th percentile of PM10 in June 2022?,Arunachal Pradesh 4096,5126,spatial_aggregation,Which city has the 2nd highest average PM2.5 in May 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ","In May 2021, identify the city with the 2nd highest average PM2.5.",Rohtak 4097,5127,spatial_aggregation,Which state has the 3rd lowest median PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report the state with the 3rd lowest median PM2.5 in October 2019.,Meghalaya 4098,5128,spatial_aggregation,Which city has the 3rd highest median PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In December 2020, which city registered the 3rd highest median PM10?",Dharuhera 4099,5129,spatial_aggregation,Which city has the 3rd highest median PM2.5 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest median PM2.5 in April 2018.,Jodhpur 4100,5130,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In August 2020, report the station with the 2nd highest 75th percentile of PM2.5.","Nathu Colony, Ballabgarh - HSPCB" 4101,5131,spatial_aggregation,Which city has the lowest 75th percentile of PM10 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest 75th percentile of PM10 in March 2024?,Tumakuru 4102,5132,spatial_aggregation,Which station has the 2nd highest average PM2.5 in December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In December 2019, identify the station with the 2nd highest average PM2.5.","Vasundhara, Ghaziabad - UPPCB" 4103,5134,spatial_aggregation,Which state has the highest 25th percentile of PM2.5 in April 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In April 2022, which state recorded the highest 25th percentile of PM2.5?",Delhi 4104,5135,spatial_aggregation,Which state has the 3rd highest median PM10 in December 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the 3rd highest median PM10 in December 2018.,Uttar Pradesh 4105,5136,spatial_aggregation,Which station has the highest 75th percentile of PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In June 2018, report the station with the highest 75th percentile of PM10.","RIICO Ind. Area III, Bhiwadi - RSPCB" 4106,5137,spatial_aggregation,Which city has the 3rd lowest median PM2.5 in April 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city displayed the 3rd lowest median PM2.5 in April 2021?,Koppal 4107,5138,spatial_aggregation,Which state has the 2nd lowest median PM2.5 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In November 2021, identify the state with the 2nd lowest median PM2.5.",Meghalaya 4108,5139,spatial_aggregation,Which state has the 2nd highest average PM2.5 in June 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest average PM2.5 in June 2022.,Delhi 4109,5140,spatial_aggregation,Which station has the 2nd highest median PM2.5 in March 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","In March 2024, which station exhibited the 2nd highest median PM2.5?","NSIT Dwarka, Delhi - CPCB" 4110,5141,spatial_aggregation,Which city has the highest average PM2.5 in June 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the highest average PM2.5 in June 2021.,Bhiwadi 4111,5142,spatial_aggregation,Which state has the 3rd highest average PM2.5 in December 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","In December 2023, report the state with the 3rd highest average PM2.5.",Bihar 4112,5143,spatial_aggregation,Which city has the 3rd highest average PM10 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest average PM10 in December 2021?,Katihar 4113,5144,spatial_aggregation,Which station has the 2nd highest 75th percentile of PM10 in April 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In April 2018, identify the station with the 2nd highest 75th percentile of PM10.","Rohini, Delhi - DPCC" 4114,5146,spatial_aggregation,Which station has the highest average PM2.5 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","In October 2018, which station registered the highest average PM2.5?","CRRI Mathura Road, Delhi - IMD" 4115,5147,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in February 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the 3rd highest 25th percentile of PM2.5 in February 2020.,Guwahati 4116,5148,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In July 2018, report the state with the 2nd lowest average PM2.5.",Telangana 4117,5149,spatial_aggregation,Which station has the 3rd highest median PM10 in June 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest median PM10 in June 2024?,"Jahangirpuri, Delhi - DPCC" 4118,5150,spatial_aggregation,Which state has the 3rd highest average PM10 in May 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 5)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ","In May 2022, identify the state with the 3rd highest average PM10.",Rajasthan 4119,5151,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report the station with the 3rd lowest 75th percentile of PM10 in January 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 4120,5152,spatial_aggregation,Which state has the 3rd lowest 75th percentile of PM2.5 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ","In October 2019, which state recorded the 3rd lowest 75th percentile of PM2.5?",Andhra Pradesh 4121,5154,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in February 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","In February 2022, report the city with the 2nd lowest median PM2.5.",Nandesari 4122,5155,spatial_aggregation,Which station has the 3rd highest average PM2.5 in October 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station displayed the 3rd highest average PM2.5 in October 2020?,"Bawana, Delhi - DPCC" 4123,5156,spatial_aggregation,Which city has the lowest median PM10 in January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ","In January 2022, identify the city with the lowest median PM10.",Nandesari 4124,5159,spatial_aggregation,Which city has the 2nd lowest median PM2.5 in June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the 2nd lowest median PM2.5 in June 2019.,Chandrapur 4125,5160,spatial_aggregation,Which city has the highest 25th percentile of PM2.5 in February 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 2)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In February 2021, report the city with the highest 25th percentile of PM2.5.",Moradabad 4126,5162,spatial_aggregation,Which city has the 3rd highest median PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In December 2021, identify the city with the 3rd highest median PM2.5.",Manesar 4127,5163,spatial_aggregation,Which state has the 2nd highest 75th percentile of PM10 in November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report the state with the 2nd highest 75th percentile of PM10 in November 2019.,Uttar Pradesh 4128,5164,spatial_aggregation,Which station has the 3rd lowest average PM10 in December 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","In December 2020, which station registered the 3rd lowest average PM10?","Sanegurava Halli, Bengaluru - KSPCB" 4129,5165,spatial_aggregation,Which state has the highest 75th percentile of PM10 in October 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state with the highest 75th percentile of PM10 in October 2018.,Delhi 4130,5166,spatial_aggregation,Which state has the lowest average PM10 in October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In October 2019, report the state with the lowest average PM10.",Kerala 4131,5168,spatial_aggregation,Which state has the highest median PM2.5 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","In August 2024, identify the state with the highest median PM2.5.",Himachal Pradesh 4132,5169,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest median PM2.5 in March 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 4133,5170,spatial_aggregation,Which state has the highest average PM10 in April 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 4)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","In April 2024, which state recorded the highest average PM10?",Delhi 4134,5171,spatial_aggregation,Which city has the lowest 75th percentile of PM2.5 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city with the lowest 75th percentile of PM2.5 in August 2020.,Aizawl 4135,5173,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in July 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2022) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state displayed the 2nd highest 25th percentile of PM2.5 in July 2022?,Rajasthan 4136,5174,spatial_aggregation,Which station has the 2nd lowest median PM2.5 in January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","In January 2023, identify the station with the 2nd lowest median PM2.5.","Sikulpuikawn, Aizawl - Mizoram PCB" 4137,5175,spatial_aggregation,Which station has the lowest 75th percentile of PM10 in September 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report the station with the lowest 75th percentile of PM10 in September 2018.,"Talcher Coalfields,Talcher - OSPCB" 4138,5176,spatial_aggregation,Which city has the 3rd highest average PM10 in January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","In January 2020, which city exhibited the 3rd highest average PM10?",Greater Noida 4139,5178,spatial_aggregation,Which state has the 2nd highest median PM2.5 in July 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","In July 2018, report the state with the 2nd highest median PM2.5.",Rajasthan 4140,5179,spatial_aggregation,Which city has the 3rd highest 25th percentile of PM2.5 in July 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 7)] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city had the 3rd highest 25th percentile of PM2.5 in July 2023?,Pali 4141,5180,spatial_aggregation,Which city has the highest average PM2.5 in March 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ","In March 2021, identify the city with the highest average PM2.5.",Bhiwadi 4142,5181,spatial_aggregation,Which city has the 2nd highest average PM10 in June 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report the city with the 2nd highest average PM10 in June 2018.,Greater Noida 4143,5182,spatial_aggregation,Which station has the 3rd highest median PM10 in September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 9)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ","In September 2019, which station registered the 3rd highest median PM10?","Dwarka-Sector 8, Delhi - DPCC" 4144,5183,spatial_aggregation,Which station has the 2nd lowest 75th percentile of PM10 in October 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station with the 2nd lowest 75th percentile of PM10 in October 2021.,"Brahmagiri, Udupi - KSPCB" 4145,5184,spatial_aggregation,Which state has the lowest 75th percentile of PM10 in January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2019) & (main_data['Timestamp'].dt.month == 1)] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","In January 2019, report the state with the lowest 75th percentile of PM10.",Kerala 4146,5185,spatial_aggregation,Which state has the 2nd highest 25th percentile of PM2.5 in December 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 12)] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 25th percentile of PM2.5 in December 2021?,Bihar 4147,5186,spatial_aggregation,Which station has the 2nd highest median PM10 in August 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ","In August 2024, identify the station with the 2nd highest median PM10.","Old City, Sri Ganganagar - RSPCB" 4148,5187,spatial_aggregation,Which city has the 3rd lowest 25th percentile of PM10 in March 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2018) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report the city with the 3rd lowest 25th percentile of PM10 in March 2018.,Rajamahendravaram 4149,5190,spatial_aggregation,Which city has the 2nd lowest median PM10 in November 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2021) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","In November 2021, report the city with the 2nd lowest median PM10.",Shillong 4150,5191,spatial_aggregation,Which state has the lowest average PM10 in March 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 3)] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state displayed the lowest average PM10 in March 2023?,Arunachal Pradesh 4151,5192,spatial_aggregation,Which city has the 3rd highest average PM2.5 in November 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","In November 2020, identify the city with the 3rd highest average PM2.5.",Bulandshahr 4152,5193,spatial_aggregation,Which station has the 2nd lowest 25th percentile of PM10 in November 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 11)] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report the station with the 2nd lowest 25th percentile of PM10 in November 2023.,"Zero Point GICI, Gangtok - SSPCB" 4153,5194,spatial_aggregation,Which station has the highest average PM10 in August 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2020) & (main_data['Timestamp'].dt.month == 8)] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","In August 2020, which station exhibited the highest average PM10?","GIDC, Nandesari - Nandesari Ind. Association" 4154,5195,spatial_aggregation,Which station has the 3rd lowest 75th percentile of PM10 in October 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2024) & (main_data['Timestamp'].dt.month == 10)] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station with the 3rd lowest 75th percentile of PM10 in October 2024.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 4155,5198,spatial_aggregation,Which state has the 2nd lowest average PM2.5 in June 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data['Timestamp'].dt.year == 2023) & (main_data['Timestamp'].dt.month == 6)] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","In June 2023, identify the state with the 2nd lowest average PM2.5.",Mizoram 4156,5200,spatial_aggregation,In which city was average PM10 the lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On January 5, 2022, which city recorded the minimum average PM10 level?",Aizawl 4157,5201,spatial_aggregation,In which state was average PM2.5 the highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Which state registered the highest average PM2.5 concentration on January 5, 2024?",Delhi 4158,5202,spatial_aggregation,In which city was average PM10 the 2nd highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On January 5, 2024, which city had the second-highest average PM10 reading?",Patna 4159,5203,spatial_aggregation,In which station was average PM2.5 the highest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Identify the station with the peak average PM2.5 level on January 5, 2022.","Loni, Ghaziabad - UPPCB" 4160,5205,spatial_aggregation,In which station was average PM2.5 the 2nd highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On January 5, 2019, which station recorded the second-highest average PM2.5 level?","Lalbagh, Lucknow - CPCB" 4161,5206,spatial_aggregation,In which station was average PM2.5 the 3rd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station that had the third-highest average PM2.5 reading on January 5, 2021.","IGSC Planetarium Complex, Patna - BSPCB" 4162,5207,spatial_aggregation,In which station was average PM10 the 2nd lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On January 5, 2022, which station registered the second-lowest average PM10 level?","Lumpyngngad, Shillong - Meghalaya PCB" 4163,5208,spatial_aggregation,In which state was average PM10 the highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","Which state experienced the highest average PM10 concentration on January 5, 2020?",Uttar Pradesh 4164,5209,spatial_aggregation,In which state was average PM2.5 the 3rd highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On January 5, 2020, which state had the third-highest average PM2.5 reading?",Haryana 4165,5211,spatial_aggregation,In which station was average PM10 the 3rd lowest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On January 5, 2019, which station recorded the third-lowest average PM10 concentration?","Model Town, Patiala - PPCB" 4166,5212,spatial_aggregation,In which station was average PM2.5 the 2nd lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest average PM2.5 level on January 5, 2020?","Bandhavgar Colony, Satna - Birla Cement" 4167,5214,spatial_aggregation,In which state was average PM10 the 2nd lowest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Identify the state that had the second-lowest average PM10 reading on January 5, 2023.",Meghalaya 4168,5215,spatial_aggregation,In which city was average PM10 the 2nd lowest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On January 5, 2019, which city recorded the second-lowest average PM10 level?",Amritsar 4169,5217,spatial_aggregation,In which state was average PM2.5 the 2nd highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On January 5, 2019, which state showed the second-highest average PM2.5 reading?",Delhi 4170,5218,spatial_aggregation,In which city was average PM2.5 the 2nd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest average PM2.5 level on January 5, 2021.",Kanpur 4171,5219,spatial_aggregation,In which state was average PM10 the 2nd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","On January 5, 2021, which state registered the second-highest average PM10 concentration?",West Bengal 4172,5220,spatial_aggregation,In which city was average PM2.5 the highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Which city had the peak average PM2.5 level on January 5, 2020?",Ghaziabad 4173,5221,spatial_aggregation,In which state was average PM2.5 the 2nd highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On January 5, 2023, which state recorded the second-highest average PM2.5 reading?",Himachal Pradesh 4174,5222,spatial_aggregation,In which city was average PM2.5 the 3rd highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city that showed the third-highest average PM2.5 concentration on January 5, 2023.",Noida 4175,5224,spatial_aggregation,In which state was average PM2.5 the 3rd highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Which state registered the third-highest average PM2.5 reading on January 5, 2023?",Bihar 4176,5225,spatial_aggregation,In which station was average PM2.5 the lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","On January 5, 2022, which station had the minimum average PM2.5 concentration?","Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 4177,5227,spatial_aggregation,In which station was average PM10 the 3rd lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On January 5, 2020, which station recorded the third-lowest average PM10 reading?","Hebbal 1st Stage, Mysuru - KSPCB" 4178,5229,spatial_aggregation,In which city was average PM10 the lowest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On January 5, 2019, which city registered the minimum average PM10 level?",Jalandhar 4179,5231,spatial_aggregation,In which city was average PM10 the highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On January 5, 2023, which city experienced the highest average PM10 concentration?",Byrnihat 4180,5232,spatial_aggregation,In which city was average PM10 the 3rd highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Which city recorded the third-highest average PM10 level on January 5, 2024?",Araria 4181,5233,spatial_aggregation,In which state was average PM10 the lowest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","On January 5, 2023, which state showed the minimum average PM10 reading?",Mizoram 4182,5234,spatial_aggregation,In which station was average PM2.5 the 3rd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest average PM2.5 concentration on January 5, 2021.","Sikulpuikawn, Aizawl - Mizoram PCB" 4183,5236,spatial_aggregation,In which city was average PM10 the 2nd highest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city had the second-highest average PM10 reading on January 5, 2022?",Saharsa 4184,5237,spatial_aggregation,In which state was average PM2.5 the 2nd highest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On January 5, 2022, which state experienced the second-highest average PM2.5 concentration?",Uttar Pradesh 4185,5238,spatial_aggregation,In which station was average PM10 the 3rd highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station that recorded the third-highest average PM10 level on January 5, 2020.","Ghusuri, Howrah - WBPCB" 4186,5239,spatial_aggregation,In which station was average PM2.5 the 2nd highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On January 5, 2018, which station showed the second-highest average PM2.5 reading?","Vasundhara, Ghaziabad - UPPCB" 4187,5241,spatial_aggregation,In which city was average PM2.5 the 3rd lowest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On January 5, 2024, which city had the third-lowest average PM2.5 level?",Ooty 4188,5242,spatial_aggregation,In which city was average PM2.5 the lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","Identify the city with the minimum average PM2.5 reading on January 5, 2020.",Satna 4189,5243,spatial_aggregation,In which city was average PM2.5 the 2nd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On January 5, 2021, which city recorded the second-lowest average PM2.5 concentration?",Aizawl 4190,5244,spatial_aggregation,In which state was average PM2.5 the 3rd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest average PM2.5 level on January 5, 2021?",Karnataka 4191,5245,spatial_aggregation,In which station was average PM10 the highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On January 5, 2021, which station registered the highest average PM10 reading?","Talkatora District Industries Center, Lucknow - CPCB" 4192,5246,spatial_aggregation,In which state was average PM2.5 the 2nd highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state that experienced the second-highest average PM2.5 concentration on January 5, 2020.",Uttar Pradesh 4193,5247,spatial_aggregation,In which state was average PM10 the 2nd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On January 5, 2021, which state had the second-lowest average PM10 level?",Mizoram 4194,5248,spatial_aggregation,In which city was average PM2.5 the 2nd lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Which city recorded the second-lowest average PM2.5 reading on January 5, 2020?",Shillong 4195,5249,spatial_aggregation,In which station was average PM10 the highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On January 5, 2018, which station showed the highest average PM10 concentration?","North Campus, DU, Delhi - IMD" 4196,5250,spatial_aggregation,In which state was average PM10 the 3rd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state with the third-highest average PM10 level on January 5, 2021.",Assam 4197,5251,spatial_aggregation,In which city was average PM10 the 3rd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On January 5, 2021, which city registered the third-lowest average PM10 reading?",Aizawl 4198,5252,spatial_aggregation,In which state was average PM10 the 3rd lowest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state experienced the third-lowest average PM10 concentration on January 5, 2024?",Kerala 4199,5254,spatial_aggregation,In which station was average PM2.5 the 3rd lowest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station that recorded the third-lowest average PM2.5 reading on January 5, 2024.","Bombay Castel, Ooty - TNPCB" 4200,5255,spatial_aggregation,In which city was average PM10 the 2nd lowest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On January 5, 2018, which city showed the second-lowest average PM10 concentration?",Nashik 4201,5256,spatial_aggregation,In which city was average PM2.5 the 2nd lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Which city registered the second-lowest average PM2.5 level on January 5, 2022?",Shillong 4202,5257,spatial_aggregation,In which station was average PM10 the 3rd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On January 5, 2021, which station experienced the third-lowest average PM10 reading?","Perungudi, Chennai - TNPCB" 4203,5258,spatial_aggregation,In which station was average PM10 the 2nd highest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","Identify the station with the second-highest average PM10 concentration on January 5, 2022.","Loni, Ghaziabad - UPPCB" 4204,5259,spatial_aggregation,In which station was average PM10 the lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On January 5, 2022, which station had the minimum average PM10 level?","Sikulpuikawn, Aizawl - Mizoram PCB" 4205,5261,spatial_aggregation,In which city was average PM2.5 the 3rd lowest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On January 5, 2018, which city showed the third-lowest average PM2.5 concentration?",Mandi Gobindgarh 4206,5262,spatial_aggregation,In which city was average PM10 the highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city that registered the highest average PM10 level on January 5, 2024.",Sri Ganganagar 4207,5264,spatial_aggregation,In which state was average PM10 the lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state had the lowest average PM10 concentration on January 5, 2020?",Tamil Nadu 4208,5265,spatial_aggregation,In which station was average PM10 the lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On January 5, 2020, which station recorded the minimum average PM10 level?","Urban, Chamarajanagar - KSPCB" 4209,5266,spatial_aggregation,In which station was average PM2.5 the lowest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Identify the station with the lowest average PM2.5 reading on January 5, 2018.","MIDC Khutala, Chandrapur - MPCB" 4210,5267,spatial_aggregation,In which station was average PM2.5 the 3rd highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","On January 5, 2020, which station showed the third-highest average PM2.5 concentration?","Nehru Nagar, Delhi - DPCC" 4211,5268,spatial_aggregation,In which city was average PM2.5 the 3rd highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Which city registered the third-highest average PM2.5 level on January 5, 2018?",Muzaffarpur 4212,5269,spatial_aggregation,In which state was average PM2.5 the lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On January 5, 2021, which state experienced the minimum average PM2.5 reading?",Mizoram 4213,5270,spatial_aggregation,In which state was average PM10 the 2nd lowest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Identify the state that had the second-lowest average PM10 concentration on January 5, 2024.",Sikkim 4214,5271,spatial_aggregation,In which city was average PM10 the 3rd highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On January 5, 2018, which city recorded the third-highest average PM10 level?",Delhi 4215,5272,spatial_aggregation,In which state was average PM2.5 the lowest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the lowest average PM2.5 reading on January 5, 2023?",Mizoram 4216,5273,spatial_aggregation,In which city was average PM2.5 the 3rd highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On January 5, 2019, which city registered the third-highest average PM2.5 concentration?",Lucknow 4217,5274,spatial_aggregation,In which city was average PM2.5 the 3rd highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest average PM2.5 level on January 5, 2024.",Sri Ganganagar 4218,5275,spatial_aggregation,In which city was average PM10 the 3rd highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On January 5, 2019, which city experienced the third-highest average PM10 reading?",Delhi 4219,5276,spatial_aggregation,In which station was average PM2.5 the lowest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Which station had the minimum average PM2.5 concentration on January 5, 2019?","Bandhavgar Colony, Satna - Birla Cement" 4220,5278,spatial_aggregation,In which state was average PM2.5 the 3rd highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state that showed the third-highest average PM2.5 reading on January 5, 2019.",Uttar Pradesh 4221,5279,spatial_aggregation,In which station was average PM2.5 the highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","On January 5, 2018, which station registered the highest average PM2.5 concentration?","DTU, Delhi - CPCB" 4222,5280,spatial_aggregation,In which city was average PM10 the lowest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Which city experienced the minimum average PM10 level on January 5, 2024?",Nandesari 4223,5281,spatial_aggregation,In which station was average PM10 the 2nd lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On January 5, 2020, which station had the second-lowest average PM10 reading?","Manali Village, Chennai - TNPCB" 4224,5282,spatial_aggregation,In which state was average PM2.5 the highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest average PM2.5 concentration on January 5, 2023.",Delhi 4225,5284,spatial_aggregation,In which station was average PM10 the highest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest average PM10 reading on January 5, 2019?","Arya Nagar, Bahadurgarh - HSPCB" 4226,5285,spatial_aggregation,In which station was average PM2.5 the 2nd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On January 5, 2021, which station registered the second-lowest average PM2.5 concentration?","Vidayagiri, Bagalkot - KSPCB" 4227,5286,spatial_aggregation,In which city was average PM10 the 2nd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city that experienced the second-highest average PM10 level on January 5, 2021.",Singrauli 4228,5287,spatial_aggregation,In which state was average PM10 the 2nd lowest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On January 5, 2020, which state had the second-lowest average PM10 reading?",Andhra Pradesh 4229,5288,spatial_aggregation,In which city was average PM10 the 3rd lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Which city recorded the third-lowest average PM10 concentration on January 5, 2022?",Nandesari 4230,5289,spatial_aggregation,In which city was average PM2.5 the 3rd lowest on January 5 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2019) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On January 5, 2019, which city showed the third-lowest average PM2.5 level?",Amritsar 4231,5290,spatial_aggregation,In which station was average PM2.5 the 3rd lowest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest average PM2.5 reading on January 5, 2023.","GIDC, Nandesari - Nandesari Ind. Association" 4232,5291,spatial_aggregation,In which city was average PM10 the 2nd lowest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On January 5, 2021, which city registered the second-lowest average PM10 concentration?",Shillong 4233,5292,spatial_aggregation,In which state was average PM2.5 the 3rd highest on January 5 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2021) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Which state experienced the third-highest average PM2.5 level on January 5, 2021?",West Bengal 4234,5293,spatial_aggregation,In which state was average PM10 the highest on January 5 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2023) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On January 5, 2023, which state had the highest average PM10 reading?",Delhi 4235,5294,spatial_aggregation,In which state was average PM10 the 3rd highest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state that recorded the third-highest average PM10 concentration on January 5, 2018.",Uttar Pradesh 4236,5296,spatial_aggregation,In which state was average PM2.5 the 3rd lowest on January 5 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2018) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Which state registered the third-lowest average PM2.5 reading on January 5, 2018?",Gujarat 4237,5297,spatial_aggregation,In which station was average PM10 the 3rd lowest on January 5 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2022) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On January 5, 2022, which station experienced the third-lowest average PM10 concentration?","GIDC, Nandesari - Nandesari Ind. Association" 4238,5299,spatial_aggregation,In which city was average PM10 the highest on January 5 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2020) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On January 5, 2020, which city had the highest average PM10 reading?",Panipat 4239,5301,spatial_aggregation,In which city was average PM2.5 the 2nd highest on January 5 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year == 2024) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.day == 5)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On January 5, 2024, which city showed the second-highest average PM2.5 level?",Saharsa 4240,5303,spatial_aggregation,Which station recorded the lowest PM10 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Throughout all recorded New Year's Eves, which station documented the absolute lowest PM10 levels?","Lumpyngngad, Shillong - Meghalaya PCB" 4241,5305,spatial_aggregation,Which station recorded the 2nd highest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ",Which station reported the second-highest PM2.5 readings on any New Year's Eve to date?,"Anand Vihar, Delhi - DPCC" 4242,5306,spatial_aggregation,Which station recorded the 3rd lowest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Considering all New Year's Eves, which station had the third-lowest recorded PM2.5 levels?","Girls College, Sivasagar - PCBA" 4243,5307,spatial_aggregation,Which city recorded the 3rd highest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On any New Year's Eve in the records, which city experienced the third-highest PM2.5 concentrations?",Delhi 4244,5308,spatial_aggregation,Which station recorded the 3rd lowest PM10 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ",Which station documented the third-lowest PM10 levels across all New Year's Eves?,"Meelavittan, Thoothukudi - TNPCB" 4245,5310,spatial_aggregation,Which city recorded the highest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the absolute highest PM2.5 levels on any New Year's Eve.,Begusarai 4246,5311,spatial_aggregation,Which state recorded the 2nd lowest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Throughout all New Year's Eves, which state registered the second-lowest PM2.5 concentrations?",Jammu and Kashmir 4247,5314,spatial_aggregation,Which station recorded the lowest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","On any New Year's Eve in the records, which station experienced the absolute lowest PM2.5 concentrations?","Sikulpuikawn, Aizawl - Mizoram PCB" 4248,5315,spatial_aggregation,Which state recorded the highest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ",Which station documented the highest PM2.5 levels across all New Year's Eves?,Delhi 4249,5316,spatial_aggregation,Which state recorded the 3rd highest PM2.5 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Among all New Year's Eves, which state showed the third-highest PM2.5 readings?",Bihar 4250,5317,spatial_aggregation,Which station recorded the 3rd highest PM10 levels on New Year’s Eve ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 12) & (main_data[""Timestamp""].dt.day == 31)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third-highest PM10 levels on any New Year's Eve.,"Anand Vihar, Delhi - DPCC" 4251,5319,spatial_aggregation,Which state reported the highest PM2.5 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""state""]) true_code() ","Which state registered the highest PM2.5 values on January 14, 2024?",Delhi 4252,5320,spatial_aggregation,Which city reported the lowest PM10 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[0][""city""]) true_code() ","On January 14, 2024, which city had the lowest PM10 readings?",Satna 4253,5321,spatial_aggregation,Which station reported the highest PM2.5 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""station""]) true_code() ","Identify the station with the peak PM2.5 measurements on January 14, 2022.","Nehru Nagar, Delhi - DPCC" 4254,5322,spatial_aggregation,Which city reported the 2nd highest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""city""]) true_code() ","Which city showed the second-highest PM2.5 values on January 14, 2021?",Delhi 4255,5323,spatial_aggregation,Which station reported the lowest PM2.5 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""station""]) true_code() ","On January 14, 2019, which station recorded the lowest PM2.5 measurements?","Bandhavgar Colony, Satna - Birla Cement" 4256,5324,spatial_aggregation,Which station reported the 2nd highest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""station""]) true_code() ","Identify the station that had the second-highest PM2.5 readings on January 14, 2021.","Nehru Nagar, Delhi - DPCC" 4257,5325,spatial_aggregation,Which station reported the 3rd highest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""station""]) true_code() ","On January 14, 2022, which station registered the third-highest PM10 measurements?","Anand Vihar, Delhi - DPCC" 4258,5327,spatial_aggregation,Which state reported the 2nd highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""state""]) true_code() ","On January 14, 2020, which state had the second-highest PM2.5 readings?",Madhya Pradesh 4259,5330,spatial_aggregation,Which station reported the 3rd highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""station""]) true_code() ","Which station showed the third-highest PM2.5 measurements on January 14, 2020?","Ghusuri, Howrah - WBPCB" 4260,5331,spatial_aggregation,Which city reported the 3rd lowest PM2.5 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""city""]) true_code() ","On January 14, 2023, which city registered the third-lowest PM2.5 values?",Ramanathapuram 4261,5332,spatial_aggregation,Which state reported the 3rd highest PM10 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""state""]) true_code() ","Identify the state that had the third-highest PM10 readings on January 14, 2023.",Bihar 4262,5333,spatial_aggregation,Which city reported the 3rd highest PM10 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""city""]) true_code() ","On January 14, 2019, which city recorded the third-highest PM10 measurements?",Howrah 4263,5334,spatial_aggregation,Which station reported the 2nd highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""station""]) true_code() ","Which station experienced the second-highest PM2.5 values on January 14, 2018?","Ardhali Bazar, Varanasi - UPPCB" 4264,5335,spatial_aggregation,Which state reported the lowest PM2.5 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""state""]) true_code() ","On January 14, 2019, which state showed the lowest PM2.5 readings?",Madhya Pradesh 4265,5336,spatial_aggregation,Which city reported the lowest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""city""]) true_code() ","Identify the city with the lowest PM2.5 measurements on January 14, 2021.",Bengaluru 4266,5337,spatial_aggregation,Which state reported the lowest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[0][""state""]) true_code() ","On January 14, 2021, which state registered the lowest PM10 values?",Karnataka 4267,5338,spatial_aggregation,Which city reported the highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""city""]) true_code() ","Which city had the peak PM2.5 measurements on January 14, 2020?",Lucknow 4268,5339,spatial_aggregation,Which state reported the lowest PM2.5 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""state""]) true_code() ","On January 14, 2023, which state recorded the lowest PM2.5 readings?",Assam 4269,5341,spatial_aggregation,Which city reported the 2nd highest PM10 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""city""]) true_code() ","On January 14, 2023, which city experienced the second-highest PM10 measurements?",Saharsa 4270,5342,spatial_aggregation,Which state reported the 2nd highest PM2.5 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""state""]) true_code() ","Which state registered the second-highest PM2.5 readings on January 14, 2023?",Assam 4271,5343,spatial_aggregation,Which station reported the 3rd lowest PM2.5 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""station""]) true_code() ","On January 14, 2022, which station had the third-lowest PM2.5 values?","GIDC, Nandesari - Nandesari Ind. Association" 4272,5345,spatial_aggregation,Which station reported the 2nd lowest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[1][""station""]) true_code() ","On January 14, 2020, which station recorded the second-lowest PM10 readings?","Hardev Nagar, Bathinda - PPCB" 4273,5346,spatial_aggregation,Which state reported the highest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""state""]) true_code() ","Which state showed the highest PM10 values on January 14, 2021?",Delhi 4274,5347,spatial_aggregation,Which city reported the 3rd lowest PM10 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[2][""city""]) true_code() ","On January 14, 2019, which city registered the third-lowest PM10 measurements?",Khanna 4275,5348,spatial_aggregation,Which city reported the 3rd highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""city""]) true_code() ","Identify the city that had the third-highest PM2.5 readings on January 14, 2018.",Delhi 4276,5349,spatial_aggregation,Which city reported the highest PM10 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""city""]) true_code() ","On January 14, 2023, which city experienced the highest PM10 values?",Patna 4277,5351,spatial_aggregation,Which state reported the 3rd lowest PM10 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[2][""state""]) true_code() ","On January 14, 2023, which state showed the third-lowest PM10 readings?",Gujarat 4278,5352,spatial_aggregation,Which station reported the 2nd lowest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest PM2.5 values on January 14, 2021.","Plammoodu, Thiruvananthapuram - Kerala PCB" 4279,5354,spatial_aggregation,Which city reported the lowest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[0][""city""]) true_code() ","Which city had the lowest PM10 readings on January 14, 2022?",Nandesari 4280,5355,spatial_aggregation,Which state reported the lowest PM2.5 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""state""]) true_code() ","On January 14, 2022, which state experienced the lowest PM2.5 values?",Mizoram 4281,5356,spatial_aggregation,Which station reported the 2nd highest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""station""]) true_code() ","Identify the station that recorded the second-highest PM10 measurements on January 14, 2020.","Suryakiran Bhawan NCL, Singrauli - MPPCB" 4282,5357,spatial_aggregation,Which station reported the lowest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""station""]) true_code() ","On January 14, 2018, which station showed the lowest PM2.5 readings?","BWSSB Kadabesanahalli, Bengaluru - CPCB" 4283,5358,spatial_aggregation,Which state reported the 3rd highest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""state""]) true_code() ","Which state registered the third-highest PM10 values on January 14, 2022?",Delhi 4284,5359,spatial_aggregation,Which city reported the 2nd lowest PM2.5 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""city""]) true_code() ","On January 14, 2024, which city had the second-lowest PM2.5 measurements?",Satna 4285,5360,spatial_aggregation,Which city reported the 3rd lowest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""city""]) true_code() ","Identify the city with the third-lowest PM2.5 readings on January 14, 2020.",Eloor 4286,5361,spatial_aggregation,Which city reported the 3rd highest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""city""]) true_code() ","On January 14, 2021, which city recorded the third-highest PM2.5 values?",Delhi 4287,5362,spatial_aggregation,Which state reported the 2nd lowest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""state""]) true_code() ","Which state showed the second-lowest PM2.5 measurements on January 14, 2021?",Kerala 4288,5363,spatial_aggregation,Which station reported the highest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""station""]) true_code() ","On January 14, 2021, which station registered the highest PM10 readings?","Chandni Chowk, Delhi - IITM" 4289,5364,spatial_aggregation,Which state reported the lowest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""state""]) true_code() ","Identify the state that experienced the lowest PM2.5 values on January 14, 2020.",Madhya Pradesh 4290,5365,spatial_aggregation,Which state reported the 3rd highest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""state""]) true_code() ","On January 14, 2021, which state had the third-highest PM10 measurements?",Delhi 4291,5366,spatial_aggregation,Which city reported the 3rd highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""city""]) true_code() ","Which city recorded the third-highest PM2.5 readings on January 14, 2020?",Howrah 4292,5367,spatial_aggregation,Which station reported the highest PM10 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""station""]) true_code() ","On January 14, 2018, which station showed the highest PM10 values?","Anand Vihar, Delhi - DPCC" 4293,5368,spatial_aggregation,Which state reported the 2nd highest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest PM10 measurements on January 14, 2021.",Haryana 4294,5371,spatial_aggregation,Which station reported the 2nd highest PM10 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""station""]) true_code() ","On January 14, 2019, which station had the second-highest PM10 measurements?","Talcher Coalfields,Talcher - OSPCB" 4295,5375,spatial_aggregation,Which station reported the 2nd lowest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[1][""station""]) true_code() ","On January 14, 2021, which station experienced the second-lowest PM10 readings?","Plammoodu, Thiruvananthapuram - Kerala PCB" 4296,5376,spatial_aggregation,Which station reported the lowest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[0][""station""]) true_code() ","Identify the station with the lowest PM10 values on January 14, 2022.","GIDC, Nandesari - Nandesari Ind. Association" 4297,5377,spatial_aggregation,Which station reported the 3rd lowest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[2][""station""]) true_code() ","On January 14, 2022, which station had the third-lowest PM10 measurements?","Perungudi, Chennai - TNPCB" 4298,5378,spatial_aggregation,Which station reported the highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""station""]) true_code() ","Which station recorded the highest PM2.5 readings on January 14, 2020?","Talkatora District Industries Center, Lucknow - CPCB" 4299,5379,spatial_aggregation,Which city reported the 2nd lowest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""city""]) true_code() ","On January 14, 2018, which city showed the second-lowest PM2.5 values?",Thiruvananthapuram 4300,5380,spatial_aggregation,Which city reported the highest PM10 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""city""]) true_code() ","Identify the city that registered the highest PM10 measurements on January 14, 2024.",Faridabad 4301,5382,spatial_aggregation,Which state reported the 3rd lowest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[2][""state""]) true_code() ","Which state had the third-lowest PM10 values on January 14, 2020?",Maharashtra 4302,5383,spatial_aggregation,Which station reported the 3rd lowest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[2][""station""]) true_code() ","On January 14, 2020, which station recorded the third-lowest PM10 measurements?","Airoli, Navi Mumbai - MPCB" 4303,5384,spatial_aggregation,Which station reported the 3rd lowest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest PM2.5 readings on January 14, 2018.","BTM Layout, Bengaluru - CPCB" 4304,5385,spatial_aggregation,Which station reported the 2nd highest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""station""]) true_code() ","On January 14, 2020, which station showed the second-highest PM2.5 values?","Suryakiran Bhawan NCL, Singrauli - MPPCB" 4305,5386,spatial_aggregation,Which city reported the 2nd highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""city""]) true_code() ","Which city registered the second-highest PM2.5 measurements on January 14, 2018?",Varanasi 4306,5387,spatial_aggregation,Which state reported the 3rd lowest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""state""]) true_code() ","On January 14, 2021, which state experienced the third-lowest PM2.5 readings?",Karnataka 4307,5388,spatial_aggregation,Which state reported the 3rd highest PM10 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""state""]) true_code() ","Identify the state that had the third-highest PM10 values on January 14, 2024.",Delhi 4308,5390,spatial_aggregation,Which state reported the 3rd lowest PM2.5 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[2][""state""]) true_code() ","Which state showed the third-lowest PM2.5 readings on January 14, 2023?",Tamil Nadu 4309,5391,spatial_aggregation,Which city reported the 2nd highest PM2.5 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""city""]) true_code() ","On January 14, 2019, which city registered the second-highest PM2.5 values?",Howrah 4310,5392,spatial_aggregation,Which city reported the 2nd highest PM2.5 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest PM2.5 measurements on January 14, 2024.",Delhi 4311,5393,spatial_aggregation,Which city reported the 2nd highest PM10 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""city""]) true_code() ","On January 14, 2019, which city experienced the second-highest PM10 readings?",Talcher 4312,5395,spatial_aggregation,Which city reported the highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""city""]) true_code() ","On January 14, 2018, which city recorded the highest PM2.5 measurements?",Kanpur 4313,5396,spatial_aggregation,Which state reported the 2nd highest PM2.5 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""state""]) true_code() ","Identify the state that showed the second-highest PM2.5 readings on January 14, 2019.",West Bengal 4314,5397,spatial_aggregation,Which station reported the highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-1][""station""]) true_code() ","On January 14, 2018, which station registered the highest PM2.5 values?","Nehru Nagar, Kanpur - UPPCB" 4315,5399,spatial_aggregation,Which station reported the 3rd highest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""station""]) true_code() ","On January 14, 2020, which station had the third-highest PM10 readings?","Rabindra Bharati University, Kolkata - WBPCB" 4316,5401,spatial_aggregation,Which state reported the 3rd highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""state""]) true_code() ","On January 14, 2018, which state recorded the third-highest PM2.5 measurements?",Delhi 4317,5402,spatial_aggregation,Which station reported the highest PM10 readings during January 14 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""station""]) true_code() ","Which station showed the highest PM10 readings on January 14, 2019?","Rabindra Bharati University, Kolkata - WBPCB" 4318,5403,spatial_aggregation,Which station reported the 3rd highest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-3][""station""]) true_code() ","On January 14, 2021, which station registered the third-highest PM2.5 values?","Mundka, Delhi - DPCC" 4319,5404,spatial_aggregation,Which city reported the lowest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[0][""city""]) true_code() ","Identify the city that experienced the lowest PM10 measurements on January 14, 2021.",Bengaluru 4320,5405,spatial_aggregation,Which state reported the 3rd highest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""state""]) true_code() ","On January 14, 2020, which state had the third-highest PM10 readings?",West Bengal 4321,5406,spatial_aggregation,Which city reported the 2nd lowest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[1][""city""]) true_code() ","Which city recorded the second-lowest PM10 values on January 14, 2022?",Madikeri 4322,5408,spatial_aggregation,Which station reported the 2nd lowest PM2.5 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest PM2.5 readings on January 14, 2023.","Deen Dayal Nagar, Sagar - MPPCB" 4323,5409,spatial_aggregation,Which city reported the 3rd highest PM10 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-3][""city""]) true_code() ","On January 14, 2021, which city registered the third-highest PM10 values?",Delhi 4324,5410,spatial_aggregation,Which state reported the 2nd highest PM2.5 readings during January 14 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""state""]) true_code() ","Which state experienced the second-highest PM2.5 measurements on January 14, 2021?",Delhi 4325,5411,spatial_aggregation,Which state reported the highest PM10 readings during January 14 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""state""]) true_code() ","On January 14, 2023, which state had the highest PM10 readings?",Bihar 4326,5412,spatial_aggregation,Which state reported the 2nd highest PM10 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-2][""state""]) true_code() ","Identify the state that recorded the second-highest PM10 values on January 14, 2018.",Uttar Pradesh 4327,5413,spatial_aggregation,Which state reported the 2nd highest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[-2][""state""]) true_code() ","On January 14, 2018, which state showed the second-highest PM2.5 measurements?",Uttar Pradesh 4328,5414,spatial_aggregation,Which state reported the 2nd lowest PM2.5 readings during January 14 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[1][""state""]) true_code() ","Which state registered the second-lowest PM2.5 readings on January 14, 2018?",Kerala 4329,5415,spatial_aggregation,Which station reported the 2nd lowest PM10 readings during January 14 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[1][""station""]) true_code() ","On January 14, 2022, which station experienced the second-lowest PM10 values?","Stuart Hill, Madikeri - KSPCB" 4330,5417,spatial_aggregation,Which city reported the highest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[-1][""city""]) true_code() ","On January 14, 2020, which city had the highest PM10 readings?",Howrah 4331,5418,spatial_aggregation,Which station reported the lowest PM2.5 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""station""]) true_code() ","Which station recorded the lowest PM2.5 values on January 14, 2020?","Bandhavgar Colony, Satna - Birla Cement" 4332,5419,spatial_aggregation,Which city reported the lowest PM2.5 readings during January 14 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data_sorted = data.sort_values(by=""PM2.5"") print(data_sorted.iloc[0][""city""]) true_code() ","On January 14, 2024, which city showed the lowest PM2.5 measurements?",Kunjemura 4333,5420,spatial_aggregation,Which state reported the 2nd lowest PM10 readings during January 14 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[ (main_data['Timestamp'].dt.month == 1) & (main_data['Timestamp'].dt.day == 14) & (main_data['Timestamp'].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data_sorted = data.sort_values(by=""PM10"") print(data_sorted.iloc[1][""state""]) true_code() ","Identify the state that registered the second-lowest PM10 readings on January 14, 2020.",Punjab 4334,5421,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","During March 31, 2021, which station exhibited the second-lowest 25th percentile for PM10?","PWD Juction, Kohima - NPCB" 4335,5422,spatial_aggregation,In which city was the 25th percentile of PM2.5 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2021, which city recorded the minimum 25th percentile of PM2.5?",Shillong 4336,5423,spatial_aggregation,In which station was the 25th percentile of PM2.5 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest 25th percentile for PM2.5 on March 31, 2020?","Railway Colony, Guwahati - PCBA" 4337,5424,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2020, which city had the third-lowest 25th percentile of PM2.5?",Patiala 4338,5425,spatial_aggregation,In which city was the median PM10 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest median PM10 on March 31, 2018.",Singrauli 4339,5427,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Which city showed the lowest average PM10 on March 31, 2021?",Kohima 4340,5428,spatial_aggregation,In which city was the median PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2024, which city had the third-lowest median PM2.5?",Amaravati 4341,5429,spatial_aggregation,In which state was the median PM10 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum median PM10 on March 31, 2021.",Nagaland 4342,5430,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2021, which state recorded the second-highest 25th percentile for PM2.5?",West Bengal 4343,5433,spatial_aggregation,In which station was the median PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest median PM2.5 on March 31, 2018.","Alandur Bus Depot, Chennai - CPCB" 4344,5434,spatial_aggregation,In which city was the average PM2.5 the 3rd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2020, which city recorded the third-highest average PM2.5?",Muzaffarpur 4345,5435,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 75th percentile for PM2.5 on March 31, 2023?",Puducherry 4346,5436,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2023, which city had the second-lowest 75th percentile for PM10?",Udupi 4347,5437,spatial_aggregation,In which station was the median PM10 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","Identify the station with the highest median PM10 on March 31, 2019.","Mundka, Delhi - DPCC" 4348,5438,spatial_aggregation,In which station was the 25th percentile of PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2018, which station recorded the minimum 25th percentile for PM10?","Anand Kala Kshetram, Rajamahendravaram - APPCB" 4349,5439,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the lowest 25th percentile for PM2.5 on March 31, 2022?",Puducherry 4350,5441,spatial_aggregation,In which station was the 75th percentile of PM2.5 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Identify the station with the minimum 75th percentile for PM2.5 on March 31, 2020.","Bandhavgar Colony, Satna - Birla Cement" 4351,5442,spatial_aggregation,In which city was the median PM10 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2022, which city recorded the second-lowest median PM10?",Puducherry 4352,5443,spatial_aggregation,In which state was the median PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest median PM10 on March 31, 2022?",Jammu and Kashmir 4353,5445,spatial_aggregation,In which city was the median PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Identify the city with the second-lowest median PM10 on March 31, 2019.",Rupnagar 4354,5446,spatial_aggregation,In which state was the median PM2.5 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2018, which state recorded the minimum median PM2.5?",West Bengal 4355,5447,spatial_aggregation,In which city was the median PM10 the highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Which city showed the highest median PM10 on March 31, 2021?",Udupi 4356,5448,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2019, which state had the lowest 25th percentile for PM2.5?",Punjab 4357,5449,spatial_aggregation,In which city was the average PM10 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest average PM10 on March 31, 2024.",Byrnihat 4358,5450,spatial_aggregation,In which station was the median PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2022, which station recorded the third-lowest median PM10?","Jawahar Nagar, Puducherry - PPCC" 4359,5451,spatial_aggregation,In which station was the 25th percentile of PM10 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest 25th percentile for PM10 on March 31, 2022?","Loni, Ghaziabad - UPPCB" 4360,5452,spatial_aggregation,In which station was the 75th percentile of PM10 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2023, which station had the third-highest 75th percentile for PM10?","DRCC Anandpur, Begusarai - BSPCB" 4361,5453,spatial_aggregation,In which state was the average PM10 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest average PM10 on March 31, 2021.",Rajasthan 4362,5454,spatial_aggregation,In which state was the average PM2.5 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2023, which state recorded the second-lowest average PM2.5?",Puducherry 4363,5455,spatial_aggregation,In which station was the average PM2.5 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest average PM2.5 on March 31, 2020?","Pusa, Delhi - IMD" 4364,5456,spatial_aggregation,In which state was the average PM2.5 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2023, which state had the second-highest average PM2.5?",Meghalaya 4365,5457,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest 75th percentile for PM2.5 on March 31, 2018.","Anand Kala Kshetram, Rajamahendravaram - APPCB" 4366,5458,spatial_aggregation,In which state was the median PM10 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2021, which state recorded the third-lowest median PM10?",Meghalaya 4367,5459,spatial_aggregation,In which state was the 75th percentile of PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 75th percentile for PM10 on March 31, 2019?",Assam 4368,5460,spatial_aggregation,In which state was the 25th percentile of PM10 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2022, which state had the third-highest 25th percentile for PM10?",Himachal Pradesh 4369,5461,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest 75th percentile for PM10 on March 31, 2022.",Chhapra 4370,5462,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2023, which city recorded the third-lowest 25th percentile of PM2.5?",Karauli 4371,5464,spatial_aggregation,In which station was the 25th percentile of PM2.5 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2018, which station had the lowest 25th percentile for PM2.5?","Bandhavgar Colony, Satna - Birla Cement" 4372,5466,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2021, which state recorded the lowest 25th percentile of PM2.5?",Meghalaya 4373,5467,spatial_aggregation,In which city was the median PM10 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Which city showed the highest median PM10 on March 31, 2022?",Bihar Sharif 4374,5468,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2024, which station had the second-lowest 25th percentile for PM2.5?","Civil Lines, Bareilly - UPPCB" 4375,5470,spatial_aggregation,In which station was the median PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2020, which station recorded the third-lowest median PM10?","SIDCO Kurichi, Coimbatore - TNPCB" 4376,5471,spatial_aggregation,In which city was the median PM2.5 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","Which city showed the lowest median PM2.5 on March 31, 2019?",Satna 4377,5472,spatial_aggregation,In which station was the 75th percentile of PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2020, which station had the second-lowest 75th percentile for PM10?","Model Town, Patiala - PPCB" 4378,5473,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest 75th percentile for PM2.5 on March 31, 2018.",Delhi 4379,5474,spatial_aggregation,In which city was the median PM2.5 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2020, which city recorded the minimum median PM2.5?",Satna 4380,5475,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Which city showed the third-lowest 25th percentile for PM10 on March 31, 2024?",Ariyalur 4381,5476,spatial_aggregation,In which station was the median PM2.5 the 2nd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2022, which station had the second-highest median PM2.5?","Loni, Ghaziabad - UPPCB" 4382,5477,spatial_aggregation,In which city was the average PM10 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest average PM10 on March 31, 2022.",Bihar Sharif 4383,5478,spatial_aggregation,In which state was the 25th percentile of PM10 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2024, which state recorded the highest 25th percentile for PM10?",Delhi 4384,5479,spatial_aggregation,In which state was the 75th percentile of PM2.5 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Which state showed the highest 75th percentile for PM2.5 on March 31, 2023?",Bihar 4385,5480,spatial_aggregation,In which state was the 25th percentile of PM10 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2018, which state had the highest 25th percentile for PM10?",Delhi 4386,5481,spatial_aggregation,In which station was the 25th percentile of PM10 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest 25th percentile for PM10 on March 31, 2024.","Maldahiya, Varanasi - UPPCB" 4387,5482,spatial_aggregation,In which city was the median PM10 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2020, which city recorded the second-highest median PM10?",Chennai 4388,5483,spatial_aggregation,In which state was the 25th percentile of PM2.5 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Which state showed the highest 25th percentile for PM2.5 on March 31, 2020?",Assam 4389,5484,spatial_aggregation,In which station was the median PM10 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2024, which station had the second-lowest median PM10?","Ardhali Bazar, Varanasi - UPPCB" 4390,5485,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 75th percentile for PM2.5 on March 31, 2024.","Sector-51, Gurugram - HSPCB" 4391,5486,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2019, which city recorded the third-highest 25th percentile for PM10?",Ballabgarh 4392,5487,spatial_aggregation,In which city was the median PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city showed the second-highest median PM10 on March 31, 2024?",Gurugram 4393,5488,spatial_aggregation,In which state was the 75th percentile of PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2018, which state had the third-lowest 75th percentile for PM10?",Andhra Pradesh 4394,5489,spatial_aggregation,In which city was the average PM10 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest average PM10 on March 31, 2024.",Surat 4395,5490,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2022, which station recorded the second-highest 25th percentile for PM10?","D M Colony, Bihar Sharif - BSPCB" 4396,5491,spatial_aggregation,In which state was the 75th percentile of PM10 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest 75th percentile for PM10 on March 31, 2023?",Assam 4397,5492,spatial_aggregation,In which state was the 25th percentile of PM10 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2023, which state had the third-lowest 25th percentile for PM10?",Haryana 4398,5493,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest average PM10 on March 31, 2018.",Bhiwadi 4399,5496,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2021, which city had the third-lowest 75th percentile of PM2.5?",Kohima 4400,5497,spatial_aggregation,In which city was the median PM2.5 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest median PM2.5 on March 31, 2019.",Lucknow 4401,5498,spatial_aggregation,In which station was the 75th percentile of PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2019, which station recorded the second-lowest 75th percentile for PM10?","Ratanpura, Rupnagar - Ambuja Cements" 4402,5499,spatial_aggregation,In which state was the 75th percentile of PM10 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","Which state showed the highest 75th percentile for PM10 on March 31, 2020?",Assam 4403,5500,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2022, which city had the third-lowest 75th percentile for PM10?",Udupi 4404,5501,spatial_aggregation,In which station was the average PM2.5 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest average PM2.5 on March 31, 2023.","Tarapur, Silchar - PCBA" 4405,5503,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest 25th percentile for PM2.5 on March 31, 2020?",Odisha 4406,5504,spatial_aggregation,In which state was the median PM10 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2024, which state had the second-lowest median PM10?",Sikkim 4407,5507,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest 25th percentile for PM2.5 on March 31, 2019?","RIICO Ind. Area III, Bhiwadi - RSPCB" 4408,5508,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2018, which station had the third-lowest 25th percentile for PM2.5?","Alandur Bus Depot, Chennai - CPCB" 4409,5509,spatial_aggregation,In which state was the average PM2.5 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum average PM2.5 on March 31, 2022.",Puducherry 4410,5511,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Which city showed the second-lowest 25th percentile for PM10 on March 31, 2020?",Patiala 4411,5512,spatial_aggregation,In which station was the median PM10 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2019, which station had the third-highest median PM10?","Ardhali Bazar, Varanasi - UPPCB" 4412,5513,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 25th percentile for PM2.5 on March 31, 2023.","DRCC Anandpur, Begusarai - BSPCB" 4413,5514,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2018, which city recorded the second-highest 75th percentile for PM10?",Bhiwadi 4414,5515,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest 25th percentile for PM10 on March 31, 2018?","Wazirpur, Delhi - DPCC" 4415,5517,spatial_aggregation,In which state was the average PM2.5 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest average PM2.5 on March 31, 2022.",Jammu and Kashmir 4416,5519,spatial_aggregation,In which state was the average PM10 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest average PM10 on March 31, 2019?",Uttar Pradesh 4417,5522,spatial_aggregation,In which city was the average PM2.5 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2022, which city recorded the second-lowest average PM2.5?",Aizawl 4418,5527,spatial_aggregation,In which station was the average PM2.5 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Which station showed the third-highest average PM2.5 on March 31, 2019?","Ardhali Bazar, Varanasi - UPPCB" 4419,5529,spatial_aggregation,In which station was the 75th percentile of PM2.5 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Identify the station with the minimum 75th percentile for PM2.5 on March 31, 2021.","Lumpyngngad, Shillong - Meghalaya PCB" 4420,5530,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2023, which station recorded the second-lowest average PM10?","Brahmagiri, Udupi - KSPCB" 4421,5531,spatial_aggregation,In which station was the 75th percentile of PM10 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","Which station showed the third-highest 75th percentile for PM10 on March 31, 2021?","Jai Bhim Nagar, Meerut - UPPCB" 4422,5532,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2023, which station had the third-highest 75th percentile for PM2.5?","DRCC Anandpur, Begusarai - BSPCB" 4423,5533,spatial_aggregation,In which station was the 25th percentile of PM2.5 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Identify the station with the highest 25th percentile for PM2.5 on March 31, 2023.","Central Academy for SFS, Byrnihat - PCBA" 4424,5534,spatial_aggregation,In which station was the median PM10 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2021, which station recorded the minimum median PM10?","Plammoodu, Thiruvananthapuram - Kerala PCB" 4425,5535,spatial_aggregation,In which state was the median PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the lowest median PM10 on March 31, 2018?",Andhra Pradesh 4426,5536,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2018, which state had the second-highest 25th percentile for PM10?",Haryana 4427,5538,spatial_aggregation,In which city was the average PM2.5 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2021, which city recorded the third-lowest average PM2.5?",Kohima 4428,5539,spatial_aggregation,In which state was the 25th percentile of PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the minimum 25th percentile for PM10 on March 31, 2018?",Andhra Pradesh 4429,5541,spatial_aggregation,In which state was the average PM2.5 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state with the third-highest average PM2.5 on March 31, 2024.",Haryana 4430,5542,spatial_aggregation,In which station was the median PM10 the 3rd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2019, which station recorded the third-lowest median PM10?","Padmapukur, Howrah - WBPCB" 4431,5543,spatial_aggregation,In which city was the median PM2.5 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Which city showed the third-highest median PM2.5 on March 31, 2022?",Muzaffarnagar 4432,5544,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2019, which station had the second-lowest 25th percentile for PM2.5?","More Chowk Waluj, Aurangabad - MPCB" 4433,5545,spatial_aggregation,In which state was the median PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Identify the state with the second-lowest median PM10 on March 31, 2020.",Punjab 4434,5547,spatial_aggregation,In which state was the average PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest average PM2.5 on March 31, 2018?",Tamil Nadu 4435,5548,spatial_aggregation,In which station was the 25th percentile of PM10 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2022, which station had the third-highest 25th percentile for PM10?","Mundka, Delhi - DPCC" 4436,5549,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest 75th percentile for PM10 on March 31, 2024.",Gurugram 4437,5550,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2024, which state recorded the third-highest 75th percentile of PM2.5?",Assam 4438,5552,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2023, which city had the minimum average PM10?",Karauli 4439,5554,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2023, which city recorded the second-highest average PM10?",Bihar Sharif 4440,5555,spatial_aggregation,In which station was the 75th percentile of PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Which station showed the minimum 75th percentile for PM10 on March 31, 2023?","Satyawati Vihar, Karauli - RSPCB" 4441,5556,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2024, which city had the third-lowest 75th percentile of PM2.5?",Rajamahendravaram 4442,5557,spatial_aggregation,In which state was the 75th percentile of PM10 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest 75th percentile for PM10 on March 31, 2024.",Delhi 4443,5558,spatial_aggregation,In which state was the average PM2.5 the 3rd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2020, which state recorded the third-highest average PM2.5?",Jharkhand 4444,5559,spatial_aggregation,In which city was the average PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","Which city showed the third-lowest average PM2.5 on March 31, 2024?",Amaravati 4445,5560,spatial_aggregation,In which state was the median PM2.5 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2024, which state had the highest median PM2.5?",Delhi 4446,5561,spatial_aggregation,In which state was the average PM2.5 the 2nd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest average PM2.5 on March 31, 2022.",Haryana 4447,5562,spatial_aggregation,In which state was the average PM10 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2022, which state recorded the highest average PM10?",Delhi 4448,5563,spatial_aggregation,In which state was the median PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest median PM2.5 on March 31, 2024?",Sikkim 4449,5564,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2018, which city had the second-highest 25th percentile for PM2.5?",Gaya 4450,5565,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest 25th percentile for PM2.5 on March 31, 2022.","Perungudi, Chennai - TNPCB" 4451,5566,spatial_aggregation,In which station was the average PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2019, which station recorded the second-lowest average PM2.5?","More Chowk Waluj, Aurangabad - MPCB" 4452,5568,spatial_aggregation,In which state was the 25th percentile of PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2018, which state had the third-lowest 25th percentile for PM10?",West Bengal 4453,5570,spatial_aggregation,In which city was the 75th percentile of PM2.5 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","On March 31, 2022, which city recorded the highest 75th percentile of PM2.5?",Bihar Sharif 4454,5572,spatial_aggregation,In which station was the average PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2020, which station had the third-lowest average PM10?","SIDCO Kurichi, Coimbatore - TNPCB" 4455,5573,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Identify the station with the second-highest 75th percentile for PM2.5 on March 31, 2021.","Suryakiran Bhawan NCL, Singrauli - MPPCB" 4456,5574,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2020, which state recorded the second-lowest 75th percentile of PM2.5?",Punjab 4457,5575,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Which city showed the second-lowest 25th percentile for PM2.5 on March 31, 2022?",Chennai 4458,5577,spatial_aggregation,In which station was the median PM10 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest median PM10 on March 31, 2022.","Velachery Res. Area, Chennai - CPCB" 4459,5578,spatial_aggregation,In which station was the median PM2.5 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2020, which station recorded the second-lowest median PM2.5?","Hardev Nagar, Bathinda - PPCB" 4460,5579,spatial_aggregation,In which state was the 75th percentile of PM2.5 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the minimum 75th percentile for PM2.5 on March 31, 2022?",Puducherry 4461,5580,spatial_aggregation,In which state was the average PM2.5 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2019, which state had the second-highest average PM2.5?",Odisha 4462,5581,spatial_aggregation,In which state was the 75th percentile of PM10 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum 75th percentile for PM10 on March 31, 2022.",Meghalaya 4463,5582,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2023, which city recorded the third-lowest 75th percentile of PM2.5?",Karauli 4464,5583,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Which city showed the second-lowest 75th percentile for PM10 on March 31, 2020?",Patiala 4465,5584,spatial_aggregation,In which city was the median PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2020, which city had the third-lowest median PM10?",Coimbatore 4466,5585,spatial_aggregation,In which city was the median PM10 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Identify the city with the minimum median PM10 on March 31, 2019.",Aurangabad 4467,5586,spatial_aggregation,In which station was the median PM2.5 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2023, which station recorded the second-lowest median PM2.5?","Tarapur, Silchar - PCBA" 4468,5588,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2020, which city had the third-lowest 25th percentile for PM10?",Coimbatore 4469,5589,spatial_aggregation,In which station was the 25th percentile of PM2.5 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Identify the station with the minimum 25th percentile for PM2.5 on March 31, 2020.","Bandhavgar Colony, Satna - Birla Cement" 4470,5590,spatial_aggregation,In which state was the median PM2.5 the lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2024, which state recorded the minimum median PM2.5?",Puducherry 4471,5591,spatial_aggregation,In which state was the 25th percentile of PM10 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the lowest 25th percentile for PM10 on March 31, 2020?",Punjab 4472,5592,spatial_aggregation,In which city was the median PM10 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2019, which city had the third-highest median PM10?",Ballabgarh 4473,5594,spatial_aggregation,In which state was the median PM10 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2021, which state recorded the second-lowest median PM10?",Puducherry 4474,5595,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest 25th percentile for PM10 on March 31, 2024?",Meghalaya 4475,5597,spatial_aggregation,In which station was the median PM2.5 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Identify the station with the second-highest median PM2.5 on March 31, 2020.","Pusa, Delhi - IMD" 4476,5598,spatial_aggregation,In which city was the median PM2.5 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2023, which city recorded the third-lowest median PM2.5?",Karauli 4477,5599,spatial_aggregation,In which state was the average PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest average PM10 on March 31, 2019?",Assam 4478,5600,spatial_aggregation,In which state was the average PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2020, which state had the second-lowest average PM10?",Chandigarh 4479,5601,spatial_aggregation,In which city was the average PM10 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Identify the city with the third-lowest average PM10 on March 31, 2021.",Thiruvananthapuram 4480,5602,spatial_aggregation,In which city was the median PM2.5 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2021, which city recorded the second-highest median PM2.5?",Jodhpur 4481,5603,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city showed the second-highest average PM10 on March 31, 2020?",Chennai 4482,5604,spatial_aggregation,In which city was the average PM2.5 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2021, which city had the minimum average PM2.5?",Shillong 4483,5605,spatial_aggregation,In which city was the median PM10 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest median PM10 on March 31, 2024.",Surat 4484,5606,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2021, which city recorded the second-highest average PM10?",Katni 4485,5607,spatial_aggregation,In which station was the average PM2.5 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest average PM2.5 on March 31, 2019?","RIICO Ind. Area III, Bhiwadi - RSPCB" 4486,5608,spatial_aggregation,In which state was the median PM2.5 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2024, which state had the third-highest median PM2.5?",Odisha 4487,5610,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2019, which station recorded the second-lowest 25th percentile for PM10?","Ratanpura, Rupnagar - Ambuja Cements" 4488,5611,spatial_aggregation,In which state was the median PM10 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest median PM10 on March 31, 2024?",Mizoram 4489,5612,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2024, which station had the second-highest 25th percentile for PM2.5?","Central Academy for SFS, Byrnihat - PCBA" 4490,5613,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Identify the city with the second-lowest 25th percentile for PM10 on March 31, 2019.",Rupnagar 4491,5614,spatial_aggregation,In which city was the median PM10 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2024, which city recorded the third-lowest median PM10?",Ariyalur 4492,5616,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2022, which city had the third-lowest 75th percentile of PM2.5?",Satna 4493,5618,spatial_aggregation,In which station was the median PM2.5 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2022, which station recorded the third-lowest median PM2.5?","Jawahar Nagar, Puducherry - PPCC" 4494,5620,spatial_aggregation,In which state was the 25th percentile of PM10 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2021, which state had the third-lowest 25th percentile for PM10?",Puducherry 4495,5621,spatial_aggregation,In which city was the 25th percentile of PM2.5 the highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest 25th percentile for PM2.5 on March 31, 2021.",Singrauli 4496,5622,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2023, which state recorded the second-highest 25th percentile of PM2.5?",Nagaland 4497,5624,spatial_aggregation,In which state was the average PM2.5 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2018, which state had the minimum average PM2.5?",Andhra Pradesh 4498,5625,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","Identify the city with the third-lowest 75th percentile for PM2.5 on March 31, 2018.",Thane 4499,5626,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2020, which city recorded the minimum average PM10?",Thane 4500,5627,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest average PM10 on March 31, 2024?","Ardhali Bazar, Varanasi - UPPCB" 4501,5628,spatial_aggregation,In which state was the median PM2.5 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2020, which state had the minimum median PM2.5?",Chandigarh 4502,5629,spatial_aggregation,In which state was the median PM10 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state with the third-highest median PM10 on March 31, 2024.",Bihar 4503,5630,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2019, which station recorded the second-highest 25th percentile for PM10?","Suryakiran Bhawan NCL, Singrauli - MPPCB" 4504,5633,spatial_aggregation,In which state was the median PM2.5 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest median PM2.5 on March 31, 2020.",Odisha 4505,5634,spatial_aggregation,In which state was the median PM10 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2018, which state recorded the third-highest median PM10?",Uttar Pradesh 4506,5635,spatial_aggregation,In which state was the 75th percentile of PM10 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 75th percentile for PM10 on March 31, 2023?",Uttarakhand 4507,5636,spatial_aggregation,In which station was the median PM10 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2023, which station had the third-highest median PM10?","DRCC Anandpur, Begusarai - BSPCB" 4508,5638,spatial_aggregation,In which city was the average PM2.5 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2022, which city recorded the third-highest average PM2.5?",Muzaffarnagar 4509,5639,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Which city showed the minimum average PM10 on March 31, 2024?",Cuddalore 4510,5640,spatial_aggregation,In which state was the 75th percentile of PM10 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2020, which state had the lowest 75th percentile for PM10?",Mizoram 4511,5642,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2024, which state recorded the lowest 25th percentile of PM2.5?",Tamil Nadu 4512,5643,spatial_aggregation,In which state was the 75th percentile of PM2.5 the lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the minimum 75th percentile for PM2.5 on March 31, 2024?",Puducherry 4513,5644,spatial_aggregation,In which station was the 25th percentile of PM10 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2021, which station had the third-highest 25th percentile for PM10?","Jai Bhim Nagar, Meerut - UPPCB" 4514,5645,spatial_aggregation,In which state was the median PM10 the lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum median PM10 on March 31, 2024.",Puducherry 4515,5647,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest 75th percentile for PM2.5 on March 31, 2018?","Collectorate, Gaya - BSPCB" 4516,5648,spatial_aggregation,In which state was the 25th percentile of PM2.5 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2018, which state had the highest 25th percentile for PM2.5?",Gujarat 4517,5649,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 25th percentile for PM2.5 on March 31, 2018.","Wazirpur, Delhi - DPCC" 4518,5650,spatial_aggregation,In which city was the average PM2.5 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","On March 31, 2022, which city recorded the highest average PM2.5?",Bihar Sharif 4519,5651,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest 25th percentile for PM2.5 on March 31, 2021?","Velachery Res. Area, Chennai - CPCB" 4520,5652,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2019, which city had the third-lowest 25th percentile for PM10?",Chennai 4521,5653,spatial_aggregation,In which city was the 25th percentile of PM2.5 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest 25th percentile for PM2.5 on March 31, 2020.",Guwahati 4522,5654,spatial_aggregation,In which station was the 25th percentile of PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2023, which station recorded the minimum 25th percentile for PM10?","Satyawati Vihar, Karauli - RSPCB" 4523,5655,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Which city showed the third-highest 75th percentile for PM10 on March 31, 2019?",Ballabgarh 4524,5656,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2022, which city had the minimum average PM10?",Shillong 4525,5657,spatial_aggregation,In which state was the median PM10 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest median PM10 on March 31, 2019.",Odisha 4526,5658,spatial_aggregation,In which state was the 75th percentile of PM10 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2018, which state recorded the highest 75th percentile for PM10?",Delhi 4527,5659,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 75th percentile for PM2.5 on March 31, 2019?",Kerala 4528,5660,spatial_aggregation,In which station was the average PM2.5 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2024, which station had the second-highest average PM2.5?","Central Academy for SFS, Byrnihat - PCBA" 4529,5661,spatial_aggregation,In which state was the average PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest average PM2.5 on March 31, 2024.",Mizoram 4530,5663,spatial_aggregation,In which state was the 75th percentile of PM2.5 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Which state showed the highest 75th percentile for PM2.5 on March 31, 2018?",Bihar 4531,5664,spatial_aggregation,In which state was the average PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2020, which state had the third-lowest average PM10?",Punjab 4532,5665,spatial_aggregation,In which city was the median PM10 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest median PM10 on March 31, 2021.",Katni 4533,5666,spatial_aggregation,In which station was the average PM2.5 the 3rd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2024, which station recorded the third-highest average PM2.5?","Sector-51, Gurugram - HSPCB" 4534,5667,spatial_aggregation,In which station was the 25th percentile of PM10 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Which station showed the minimum 25th percentile for PM10 on March 31, 2019?","More Chowk Waluj, Aurangabad - MPCB" 4535,5668,spatial_aggregation,In which state was the median PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2024, which state had the second-highest median PM10?",Meghalaya 4536,5669,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest 25th percentile for PM10 on March 31, 2022.","Velachery Res. Area, Chennai - CPCB" 4537,5671,spatial_aggregation,In which state was the average PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest average PM10 on March 31, 2018?",Madhya Pradesh 4538,5673,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest 25th percentile for PM2.5 on March 31, 2024.",Meghalaya 4539,5674,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2018, which state recorded the second-lowest 25th percentile for PM10?",Kerala 4540,5675,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest average PM10 on March 31, 2018?","Secretariat, Amaravati - APPCB" 4541,5678,spatial_aggregation,In which city was the median PM2.5 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2022, which city recorded the third-lowest median PM2.5?",Satna 4542,5680,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2022, which city had the second-highest average PM10?",Singrauli 4543,5681,spatial_aggregation,In which state was the median PM2.5 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest median PM2.5 on March 31, 2019.",Bihar 4544,5682,spatial_aggregation,In which city was the average PM2.5 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2021, which city recorded the second-highest average PM2.5?",Jodhpur 4545,5683,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Which city showed the third-highest 75th percentile for PM2.5 on March 31, 2022?",Muzaffarnagar 4546,5686,spatial_aggregation,In which city was the 75th percentile of PM2.5 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","On March 31, 2023, which city recorded the highest 75th percentile of PM2.5?",Byrnihat 4547,5687,spatial_aggregation,In which station was the 25th percentile of PM10 the 3rd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","Which station showed the third-lowest 25th percentile for PM10 on March 31, 2019?","Padmapukur, Howrah - WBPCB" 4548,5688,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2021, which state had the second-lowest 25th percentile for PM10?",Kerala 4549,5689,spatial_aggregation,In which station was the median PM2.5 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Identify the station with the highest median PM2.5 on March 31, 2019.","Talkatora District Industries Center, Lucknow - CPCB" 4550,5690,spatial_aggregation,In which city was the 25th percentile of PM10 the highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On March 31, 2022, which city recorded the highest 25th percentile for PM10?",Bihar Sharif 4551,5691,spatial_aggregation,In which station was the median PM2.5 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest median PM2.5 on March 31, 2023?","Central Academy for SFS, Byrnihat - PCBA" 4552,5692,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 2nd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2022, which city had the second-highest 75th percentile of PM2.5?",Faridabad 4553,5693,spatial_aggregation,In which station was the 75th percentile of PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest 75th percentile for PM10 on March 31, 2022.","Jawahar Nagar, Puducherry - PPCC" 4554,5695,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city showed the second-highest 25th percentile for PM10 on March 31, 2020?",Chennai 4555,5696,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2024, which state had the second-lowest 25th percentile for PM2.5?",Uttarakhand 4556,5697,spatial_aggregation,In which city was the 25th percentile of PM2.5 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest 25th percentile for PM2.5 on March 31, 2019.",Bhiwadi 4557,5699,spatial_aggregation,In which state was the average PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest average PM10 on March 31, 2018?",West Bengal 4558,5700,spatial_aggregation,In which station was the median PM10 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2019, which station had the minimum median PM10?","More Chowk Waluj, Aurangabad - MPCB" 4559,5701,spatial_aggregation,In which state was the median PM2.5 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest median PM2.5 on March 31, 2024.",Assam 4560,5702,spatial_aggregation,In which station was the median PM2.5 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2022, which station recorded the second-lowest median PM2.5?","Perungudi, Chennai - TNPCB" 4561,5705,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Identify the state with the second-lowest 75th percentile for PM2.5 on March 31, 2021.",Nagaland 4562,5706,spatial_aggregation,In which state was the average PM2.5 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2021, which state recorded the minimum average PM2.5?",Meghalaya 4563,5708,spatial_aggregation,In which station was the 25th percentile of PM10 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On March 31, 2023, which station had the highest 25th percentile for PM10?","Darshan Nagar, Chhapra - BSPCB" 4564,5709,spatial_aggregation,In which city was the median PM2.5 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest median PM2.5 on March 31, 2024.",Byrnihat 4565,5710,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2018, which city recorded the third-highest 75th percentile of PM2.5?",Singrauli 4566,5711,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 75th percentile for PM2.5 on March 31, 2018?",Kerala 4567,5712,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2020, which station had the second-lowest 75th percentile for PM2.5?","Hardev Nagar, Bathinda - PPCB" 4568,5713,spatial_aggregation,In which city was the 25th percentile of PM2.5 the highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest 25th percentile for PM2.5 on March 31, 2024.",Byrnihat 4569,5714,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","On March 31, 2022, which station recorded the third-highest 25th percentile of PM2.5?","Sector 11, Faridabad - HSPCB" 4570,5715,spatial_aggregation,In which city was the median PM10 the lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Which city showed the minimum median PM10 on March 31, 2021?",Kohima 4571,5716,spatial_aggregation,In which station was the 75th percentile of PM10 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On March 31, 2019, which station had the highest 75th percentile for PM10?","Mundka, Delhi - DPCC" 4572,5717,spatial_aggregation,In which city was the average PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Identify the city with the minimum average PM10 on March 31, 2018.",Rajamahendravaram 4573,5719,spatial_aggregation,In which station was the median PM10 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest median PM10 on March 31, 2023?","D M Colony, Bihar Sharif - BSPCB" 4574,5720,spatial_aggregation,In which city was the 25th percentile of PM10 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On March 31, 2019, which city had the highest 25th percentile for PM10?",Singrauli 4575,5721,spatial_aggregation,In which state was the 75th percentile of PM2.5 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest 75th percentile for PM2.5 on March 31, 2019.",Bihar 4576,5722,spatial_aggregation,In which state was the 75th percentile of PM10 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2021, which state recorded the third-highest 75th percentile of PM10?",Madhya Pradesh 4577,5723,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Which city showed the third-lowest 75th percentile for PM10 on March 31, 2020?",Coimbatore 4578,5724,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2023, which city had the second-highest 25th percentile for PM10?",Bihar Sharif 4579,5725,spatial_aggregation,In which station was the 25th percentile of PM10 the 3rd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 25th percentile for PM10 on March 31, 2020.","Industrial Area, Hajipur - BSPCB" 4580,5726,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2019, which city recorded the second-highest 25th percentile of PM2.5?",Varanasi 4581,5727,spatial_aggregation,In which station was the 75th percentile of PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","Which station showed the second-highest 75th percentile for PM10 on March 31, 2024?","Sector 11, Faridabad - HSPCB" 4582,5729,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Identify the city with the second-lowest 75th percentile for PM10 on March 31, 2018.",Amaravati 4583,5731,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest average PM10 on March 31, 2022?","Velachery Res. Area, Chennai - CPCB" 4584,5732,spatial_aggregation,In which station was the 75th percentile of PM2.5 the highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","On March 31, 2023, which station had the highest 75th percentile for PM2.5?","Central Academy for SFS, Byrnihat - PCBA" 4585,5733,spatial_aggregation,In which station was the average PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest average PM10 on March 31, 2018.","Airoli, Navi Mumbai - MPCB" 4586,5734,spatial_aggregation,In which city was the average PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2018, which city recorded the third-lowest average PM10?",Navi Mumbai 4587,5735,spatial_aggregation,In which city was the median PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Which city showed the second-lowest median PM2.5 on March 31, 2024?",Raichur 4588,5736,spatial_aggregation,In which station was the average PM10 the highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On March 31, 2021, which station had the highest average PM10?","Brahmagiri, Udupi - KSPCB" 4589,5738,spatial_aggregation,In which station was the average PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2024, which station recorded the second-highest average PM10?","Sector 11, Faridabad - HSPCB" 4590,5739,spatial_aggregation,In which state was the average PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest average PM2.5 on March 31, 2019?",Kerala 4591,5740,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2018, which city had the second-lowest 25th percentile for PM10?",Amaravati 4592,5741,spatial_aggregation,In which station was the 75th percentile of PM10 the 3rd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 75th percentile for PM10 on March 31, 2020.","Industrial Area, Hajipur - BSPCB" 4593,5742,spatial_aggregation,In which state was the average PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","On March 31, 2022, which state recorded the third-lowest average PM10?",Jammu and Kashmir 4594,5744,spatial_aggregation,In which state was the average PM10 the 3rd highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2020, which state had the third-highest average PM10?",Tamil Nadu 4595,5745,spatial_aggregation,In which state was the 25th percentile of PM2.5 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum 25th percentile for PM2.5 on March 31, 2023.",Mizoram 4596,5746,spatial_aggregation,In which station was the median PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2024, which station recorded the third-lowest median PM2.5?","IESD Banaras Hindu University, Varanasi - UPPCB" 4597,5747,spatial_aggregation,In which state was the average PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the minimum average PM10 on March 31, 2023?",Arunachal Pradesh 4598,5748,spatial_aggregation,In which station was the median PM10 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2019, which station had the second-lowest median PM10?","Ratanpura, Rupnagar - Ambuja Cements" 4599,5749,spatial_aggregation,In which city was the average PM2.5 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city with the highest average PM2.5 on March 31, 2020.",Guwahati 4600,5750,spatial_aggregation,In which city was the median PM10 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2021, which city recorded the third-lowest median PM10?",Thiruvananthapuram 4601,5751,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","Which city showed the third-lowest 25th percentile for PM2.5 on March 31, 2018?",Thane 4602,5753,spatial_aggregation,In which city was the average PM10 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Identify the city with the third-lowest average PM10 on March 31, 2023.",Nandesari 4603,5754,spatial_aggregation,In which state was the 75th percentile of PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","On March 31, 2018, which state recorded the minimum 75th percentile for PM10?",Kerala 4604,5755,spatial_aggregation,In which station was the median PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Which station showed the lowest median PM10 on March 31, 2023?","Satyawati Vihar, Karauli - RSPCB" 4605,5756,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2019, which city had the second-lowest 75th percentile of PM2.5?",Aurangabad 4606,5757,spatial_aggregation,In which state was the average PM2.5 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest average PM2.5 on March 31, 2018.",Gujarat 4607,5759,spatial_aggregation,In which station was the 75th percentile of PM10 the highest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest 75th percentile for PM10 on March 31, 2020?","Railway Colony, Guwahati - PCBA" 4608,5760,spatial_aggregation,In which city was the median PM10 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2022, which city had the third-lowest median PM10?",Udupi 4609,5761,spatial_aggregation,In which city was the median PM2.5 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","Identify the city with the minimum median PM2.5 on March 31, 2018.",Satna 4610,5762,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On March 31, 2018, which city recorded the third-lowest 25th percentile for PM10?",Navi Mumbai 4611,5763,spatial_aggregation,In which city was the 75th percentile of PM10 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Which city showed the third-highest 75th percentile for PM10 on March 31, 2018?",Delhi 4612,5764,spatial_aggregation,In which station was the 25th percentile of PM10 the 2nd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2023, which station had the second-highest 25th percentile for PM10?","D M Colony, Bihar Sharif - BSPCB" 4613,5765,spatial_aggregation,In which state was the average PM2.5 the 3rd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest average PM2.5 on March 31, 2019.",Punjab 4614,5766,spatial_aggregation,In which state was the median PM2.5 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2021, which state recorded the third-highest median PM2.5?",West Bengal 4615,5767,spatial_aggregation,In which city was the average PM10 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city showed the second-highest average PM10 on March 31, 2019?",Varanasi 4616,5768,spatial_aggregation,In which city was the 25th percentile of PM10 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On March 31, 2018, which city had the second-highest 25th percentile for PM10?",Bhiwadi 4617,5769,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Identify the city with the third-lowest 25th percentile for PM10 on March 31, 2023.",Nandesari 4618,5770,spatial_aggregation,In which station was the median PM2.5 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On March 31, 2021, which station recorded the second-lowest median PM2.5?","Velachery Res. Area, Chennai - CPCB" 4619,5771,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Which state showed the second-highest 25th percentile for PM10 on March 31, 2019?",Uttar Pradesh 4620,5773,spatial_aggregation,In which state was the median PM2.5 the 3rd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest median PM2.5 on March 31, 2021.",Puducherry 4621,5774,spatial_aggregation,In which state was the 25th percentile of PM10 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2023, which state recorded the second-lowest 25th percentile for PM10?",Arunachal Pradesh 4622,5775,spatial_aggregation,In which station was the median PM2.5 the 2nd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest median PM2.5 on March 31, 2019?","More Chowk Waluj, Aurangabad - MPCB" 4623,5776,spatial_aggregation,In which state was the median PM2.5 the 2nd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2021, which state had the second-highest median PM2.5?",Bihar 4624,5777,spatial_aggregation,In which state was the 75th percentile of PM2.5 the lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Identify the state with the minimum 75th percentile for PM2.5 on March 31, 2020.",Chandigarh 4625,5778,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 3rd highest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2022, which state recorded the third-highest 75th percentile of PM2.5?",Uttar Pradesh 4626,5779,spatial_aggregation,In which state was the average PM2.5 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the minimum average PM2.5 on March 31, 2023?",Mizoram 4627,5780,spatial_aggregation,In which city was the 75th percentile of PM2.5 the 2nd lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2023, which city had the second-lowest 75th percentile of PM2.5?",Silchar 4628,5783,spatial_aggregation,In which city was the median PM10 the 2nd highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city showed the second-highest median PM10 on March 31, 2019?",Varanasi 4629,5784,spatial_aggregation,In which city was the average PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On March 31, 2024, which city had the second-lowest average PM2.5?",Varanasi 4630,5785,spatial_aggregation,In which state was the 75th percentile of PM2.5 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state with the third-highest 75th percentile for PM2.5 on March 31, 2023.",Gujarat 4631,5786,spatial_aggregation,In which city was the 25th percentile of PM10 the lowest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2023, which city recorded the minimum 25th percentile for PM10?",Karauli 4632,5788,spatial_aggregation,In which state was the average PM10 the highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2018, which state had the highest average PM10?",Delhi 4633,5793,spatial_aggregation,In which state was the median PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest median PM2.5 on March 31, 2018.",Tamil Nadu 4634,5794,spatial_aggregation,In which city was the 25th percentile of PM10 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2021, which city recorded the third-highest 25th percentile for PM10?",Bhiwadi 4635,5797,spatial_aggregation,In which state was the 25th percentile of PM2.5 the highest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Identify the state with the highest 25th percentile for PM2.5 on March 31, 2019.",Bihar 4636,5798,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2020, which station recorded the third-lowest 25th percentile of PM2.5?","BWSSB Kadabesanahalli, Bengaluru - CPCB" 4637,5801,spatial_aggregation,In which station was the 75th percentile of PM2.5 the highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Identify the station with the highest 75th percentile for PM2.5 on March 31, 2021.","New Industrial Town, Faridabad - HSPCB" 4638,5802,spatial_aggregation,In which station was the 75th percentile of PM10 the lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On March 31, 2018, which station recorded the minimum 75th percentile for PM10?","Anand Kala Kshetram, Rajamahendravaram - APPCB" 4639,5803,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Which state showed the second-lowest 25th percentile for PM2.5 on March 31, 2021?",Nagaland 4640,5805,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station with the third-highest 25th percentile for PM2.5 on March 31, 2021.","Police Commissionerate, Jaipur - RSPCB" 4641,5806,spatial_aggregation,In which state was the 25th percentile of PM2.5 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","On March 31, 2020, which state recorded the second-lowest 25th percentile of PM2.5?",Punjab 4642,5807,spatial_aggregation,In which state was the average PM10 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest average PM10 on March 31, 2024?",Mizoram 4643,5808,spatial_aggregation,In which city was the median PM10 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On March 31, 2018, which city had the third-highest median PM10?",Ghaziabad 4644,5810,spatial_aggregation,In which state was the average PM2.5 the 3rd highest during March 31 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On March 31, 2023, which state recorded the third-highest average PM2.5?",Assam 4645,5811,spatial_aggregation,In which station was the 25th percentile of PM10 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Which station showed the minimum 25th percentile for PM10 on March 31, 2022?","Lumpyngngad, Shillong - Meghalaya PCB" 4646,5812,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 3rd lowest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2018, which station had the third-lowest 75th percentile for PM2.5?","Alandur Bus Depot, Chennai - CPCB" 4647,5813,spatial_aggregation,In which state was the median PM2.5 the 3rd lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Identify the state with the third-lowest median PM2.5 on March 31, 2022.",Tamil Nadu 4648,5814,spatial_aggregation,In which state was the median PM10 the highest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","On March 31, 2021, which state recorded the highest median PM10?",Rajasthan 4649,5815,spatial_aggregation,In which station was the 75th percentile of PM10 the lowest during March 31 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","Which station showed the minimum 75th percentile for PM10 on March 31, 2022?","Lumpyngngad, Shillong - Meghalaya PCB" 4650,5816,spatial_aggregation,In which state was the average PM10 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2024, which state had the second-highest average PM10?",Haryana 4651,5817,spatial_aggregation,In which station was the 75th percentile of PM2.5 the 2nd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest 75th percentile for PM2.5 on March 31, 2024.","Civil Lines, Bareilly - UPPCB" 4652,5818,spatial_aggregation,In which state was the average PM2.5 the 2nd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On March 31, 2018, which state recorded the second-highest average PM2.5?",Delhi 4653,5819,spatial_aggregation,In which city was the 25th percentile of PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","Which city showed the third-lowest 25th percentile for PM2.5 on March 31, 2024?",Amaravati 4654,5820,spatial_aggregation,In which station was the average PM10 the 3rd lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2019, which station had the third-lowest average PM10?","Padmapukur, Howrah - WBPCB" 4655,5821,spatial_aggregation,In which city was the 75th percentile of PM10 the 2nd lowest during March 31 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Identify the city with the second-lowest 75th percentile for PM10 on March 31, 2021.",Nandesari 4656,5822,spatial_aggregation,In which station was the 25th percentile of PM2.5 the 3rd lowest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","On March 31, 2024, which station recorded the third-lowest 25th percentile of PM2.5?","IESD Banaras Hindu University, Varanasi - UPPCB" 4657,5824,spatial_aggregation,In which city was the 25th percentile of PM10 the lowest during March 31 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On March 31, 2019, which city had the minimum 25th percentile for PM10?",Aurangabad 4658,5825,spatial_aggregation,In which city was the average PM2.5 the 3rd highest during March 31 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest average PM2.5 on March 31, 2018.",Singrauli 4659,5828,spatial_aggregation,In which station was the median PM2.5 the 2nd highest during March 31 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On March 31, 2024, which station had the second-highest median PM2.5?","Central Academy for SFS, Byrnihat - PCBA" 4660,5829,spatial_aggregation,In which station was the average PM10 the 2nd lowest during March 31 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 3) & (main_data[""Timestamp""].dt.day == 31) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","Identify the station with the second-lowest average PM10 on March 31, 2020.","Model Town, Patiala - PPCB" 4661,5832,spatial_aggregation,In which station was PM10 the lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","During the COVID-19 lockdown of April 2020, which station had the lowest PM10 readings?","Sikulpuikawn, Aizawl - Mizoram PCB" 4662,5833,spatial_aggregation,In which city was PM10 the highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","In April 2020, amidst the COVID-19 lockdown, which city recorded the highest PM10 levels?",Kalaburagi 4663,5834,spatial_aggregation,In which station was PM2.5 the 2nd highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ",Which station registered the second-highest PM2.5 levels during the COVID-19 lockdown in April 2020?,"ITO, Delhi - CPCB" 4664,5835,spatial_aggregation,In which station was PM2.5 the 3rd lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","During April 2020's COVID-19 lockdown, which station documented the third-lowest PM2.5 concentrations?","BWSSB Kadabesanahalli, Bengaluru - CPCB" 4665,5836,spatial_aggregation,In which city was PM2.5 the 3rd highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","In the COVID-19 lockdown period of April 2020, which city experienced the third-highest PM2.5 levels?",Singrauli 4666,5837,spatial_aggregation,In which station was PM10 the 3rd lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ",Which station had the third-lowest PM10 readings during the April 2020 COVID-19 lockdown?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 4667,5838,spatial_aggregation,In which city was PM10 the 2nd lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","Amidst the COVID-19 lockdown in April 2020, which city showed the second-lowest PM10 levels?",Coimbatore 4668,5839,spatial_aggregation,In which city was PM2.5 the highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the absolute highest PM2.5 levels during the April 2020 COVID-19 lockdown.,Charkhi Dadri 4669,5840,spatial_aggregation,In which state was PM2.5 the 2nd lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","During the COVID-19 lockdown of April 2020, which state registered the second-lowest PM2.5 concentrations?",Andhra Pradesh 4670,5841,spatial_aggregation,In which city was PM10 the 3rd highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ",Which city reported the third-highest PM10 levels during the April 2020 COVID-19 lockdown?,Singrauli 4671,5842,spatial_aggregation,In which state was PM10 the 2nd lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","In April 2020's COVID-19 lockdown, which state had the second-lowest recorded PM10 levels?",Andhra Pradesh 4672,5843,spatial_aggregation,In which station was PM2.5 the lowest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","During the COVID-19 lockdown period of April 2020, which station experienced the absolute lowest PM2.5 concentrations?","DRM Office Danapur, Patna - BSPCB" 4673,5844,spatial_aggregation,In which state was PM2.5 the highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ",Which state documented the highest PM2.5 levels amidst the COVID-19 lockdown in April 2020?,Odisha 4674,5845,spatial_aggregation,In which state was PM2.5 the 3rd highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Among all states, which one showed the third-highest PM2.5 readings during the April 2020 COVID-19 lockdown?",Uttar Pradesh 4675,5846,spatial_aggregation,In which station was PM10 the 3rd highest during the COVID-19 lockdown (April 2020)?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.month == 4) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third-highest PM10 levels during the COVID-19 lockdown of April 2020.,"Suryakiran Bhawan NCL, Singrauli - MPPCB" 4676,5847,spatial_aggregation,Which city had the lowest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On August 15, 2022, which city experienced the lowest PM10 concentrations?",Maihar 4677,5848,spatial_aggregation,Which state had the highest PM2.5 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""state""]) true_code() ","Which state recorded the highest PM2.5 levels on August 15, 2024?",Assam 4678,5849,spatial_aggregation,Which city had the 2nd highest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","On August 15, 2024, which city had the second-most elevated PM10 levels?",Samastipur 4679,5851,spatial_aggregation,Which city had the 3rd highest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Which city showed the third-highest PM2.5 levels on August 15, 2021?",Mandi Gobindgarh 4680,5852,spatial_aggregation,Which station had the 2nd highest PM2.5 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","On August 15, 2019, which station registered the second-highest PM2.5 concentrations?","F-Block, Sirsa - HSPCB" 4681,5853,spatial_aggregation,Which station had the 3rd highest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station that had the third-most elevated PM2.5 levels on August 15, 2021.","Mini Secretariat, Charkhi Dadri - HSPCB" 4682,5854,spatial_aggregation,Which station had the 2nd lowest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On August 15, 2022, which station recorded the second-lowest PM10 concentrations?","Sector-19A Nerul, Navi Mumbai - IITM" 4683,5856,spatial_aggregation,Which state had the 3rd highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On August 15, 2020, which state had the third-most elevated PM2.5 levels?",Uttar Pradesh 4684,5857,spatial_aggregation,Which state had the 2nd lowest PM2.5 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""state""]) true_code() ","Identify the state with the second-lowest PM2.5 concentrations on August 15, 2022.",Manipur 4685,5858,spatial_aggregation,Which station had the 3rd lowest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On August 15, 2019, which station registered the third-lowest PM10 levels?","Shrivastav Colony, Damoh - MPPCB" 4686,5859,spatial_aggregation,Which station had the 2nd lowest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","Which station showed the second-lowest PM2.5 concentrations on August 15, 2020?","Lumpyngngad, Shillong - Meghalaya PCB" 4687,5860,spatial_aggregation,Which city had the lowest PM2.5 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","On August 15, 2023, which city recorded the minimum PM2.5 levels?",Silchar 4688,5861,spatial_aggregation,Which state had the 2nd lowest PM10 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Identify the state that had the second-lowest PM10 concentrations on August 15, 2023.",Meghalaya 4689,5862,spatial_aggregation,Which city had the 2nd lowest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On August 15, 2019, which city registered the second-lowest PM10 levels?",Thane 4690,5864,spatial_aggregation,Which state had the 2nd highest PM2.5 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On August 15, 2019, which state showed the second-most elevated PM2.5 levels?",Odisha 4691,5865,spatial_aggregation,Which city had the 2nd highest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city with the second-highest PM2.5 concentrations on August 15, 2021.",Charkhi Dadri 4692,5867,spatial_aggregation,Which city had the highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","Which city had the peak PM2.5 concentrations on August 15, 2020?",Nandesari 4693,5869,spatial_aggregation,Which city had the 3rd highest PM2.5 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city that showed the third-highest PM2.5 concentrations on August 15, 2023.",Bhilwara 4694,5870,spatial_aggregation,Which city had the 3rd highest PM10 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On August 15, 2023, which city experienced the third-highest PM10 levels?",Jorapokhar 4695,5871,spatial_aggregation,Which state had the 3rd highest PM2.5 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Which state registered the third-most elevated PM2.5 levels on August 15, 2023?",Chandigarh 4696,5872,spatial_aggregation,Which station had the lowest PM2.5 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","On August 15, 2022, which station had the minimum PM2.5 concentrations?","Sector-19A Nerul, Navi Mumbai - IITM" 4697,5873,spatial_aggregation,Which station had the 2nd highest PM2.5 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Identify the station with the second-highest PM2.5 levels on August 15, 2022.","MIT-Daudpur Kothi, Muzaffarpur - BSPCB" 4698,5874,spatial_aggregation,Which station had the 3rd lowest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On August 15, 2020, which station recorded the third-lowest PM10 concentrations?","Karve Road, Pune - MPCB" 4699,5875,spatial_aggregation,Which state had the highest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""state""]) true_code() ","Which state showed the highest PM10 levels on August 15, 2021?",Delhi 4700,5876,spatial_aggregation,Which city had the lowest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","On August 15, 2019, which city registered the minimum PM10 concentrations?",Bathinda 4701,5877,spatial_aggregation,Which city had the 2nd lowest PM2.5 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Identify the city that had the second-lowest PM2.5 levels on August 15, 2018.",Kolkata 4702,5878,spatial_aggregation,Which city had the highest PM10 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On August 15, 2023, which city experienced the highest PM10 concentrations?",Bettiah 4703,5879,spatial_aggregation,Which city had the 3rd highest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","Which city recorded the third-highest PM10 levels on August 15, 2024?",Rajgir 4704,5881,spatial_aggregation,Which station had the 3rd lowest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest PM2.5 concentrations on August 15, 2021.","ICRISAT Patancheru, Hyderabad - TSPCB" 4705,5882,spatial_aggregation,Which state had the 2nd highest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","On August 15, 2022, which state registered the second-highest PM10 levels?",Rajasthan 4706,5883,spatial_aggregation,Which city had the 2nd highest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Which city had the second-most elevated PM10 concentrations on August 15, 2022?",Visakhapatnam 4707,5884,spatial_aggregation,Which state had the 2nd highest PM2.5 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","On August 15, 2022, which state experienced the second-highest PM2.5 levels?",Rajasthan 4708,5885,spatial_aggregation,Which station had the 3rd highest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","Identify the station that recorded the third-highest PM10 concentrations on August 15, 2020.","Sector-51, Gurugram - HSPCB" 4709,5888,spatial_aggregation,Which city had the 3rd lowest PM2.5 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On August 15, 2024, which city had the third-lowest PM2.5 levels?",Amaravati 4710,5889,spatial_aggregation,Which city had the lowest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","Identify the city with the minimum PM2.5 levels on August 15, 2020.",Shillong 4711,5890,spatial_aggregation,Which city had the 2nd lowest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","On August 15, 2021, which city recorded the second-lowest PM2.5 concentrations?",Koppal 4712,5891,spatial_aggregation,Which state had the 3rd lowest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""state""]) true_code() ","Which state showed the third-lowest PM2.5 levels on August 15, 2021?",Meghalaya 4713,5892,spatial_aggregation,Which station had the highest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On August 15, 2021, which station registered the highest PM10 levels?","Anand Vihar, Delhi - DPCC" 4714,5893,spatial_aggregation,Which state had the 2nd highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state that experienced the second-highest PM2.5 concentrations on August 15, 2020.",Delhi 4715,5894,spatial_aggregation,Which state had the 2nd lowest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","On August 15, 2021, which state had the second-lowest PM10 levels?",Meghalaya 4716,5895,spatial_aggregation,Which city had the 2nd lowest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""city""]) true_code() ","Which city recorded the second-lowest PM2.5 levels on August 15, 2020?",Hubballi 4717,5896,spatial_aggregation,Which station had the highest PM10 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","On August 15, 2018, which station showed the highest PM10 concentrations?","Indira Colony Vistar, Pali - RSPCB" 4718,5898,spatial_aggregation,Which city had the 3rd lowest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","On August 15, 2021, which city registered the third-lowest PM10 levels?",Udupi 4719,5899,spatial_aggregation,Which state had the 3rd lowest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Which state experienced the third-lowest PM10 concentrations on August 15, 2024?",Manipur 4720,5900,spatial_aggregation,Which station had the 3rd highest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""station""]) true_code() ","On August 15, 2019, which station had the third-most elevated PM10 levels?","RIICO Ind. Area III, Bhiwadi - RSPCB" 4721,5901,spatial_aggregation,Which station had the 3rd lowest PM2.5 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station that recorded the third-lowest PM2.5 levels on August 15, 2024.","Sanjay Palace, Agra - UPPCB" 4722,5902,spatial_aggregation,Which city had the 2nd lowest PM10 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On August 15, 2018, which city showed the second-lowest PM10 concentrations?",Kolkata 4723,5904,spatial_aggregation,Which station had the 3rd lowest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On August 15, 2021, which station experienced the third-lowest PM10 levels?","Brahmagiri, Udupi - KSPCB" 4724,5905,spatial_aggregation,Which station had the 2nd highest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""station""]) true_code() ","Identify the station with the second-highest PM10 concentrations on August 15, 2022.","Karve Road, Pune - MPCB" 4725,5906,spatial_aggregation,Which station had the lowest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On August 15, 2022, which station had the minimum PM10 levels?","Navy Nagar-Colaba, Mumbai - IITM" 4726,5907,spatial_aggregation,Which station had the highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","Which station recorded the highest PM2.5 levels on August 15, 2020?","GIDC, Nandesari - Nandesari Ind. Association" 4727,5908,spatial_aggregation,Which city had the 3rd lowest PM2.5 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""city""]) true_code() ","On August 15, 2018, which city showed the third-lowest PM2.5 concentrations?",Chikkaballapur 4728,5909,spatial_aggregation,Which city had the highest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","Identify the city that registered the highest PM10 levels on August 15, 2024.",Byrnihat 4729,5910,spatial_aggregation,Which city had the lowest PM2.5 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""city""]) true_code() ","On August 15, 2022, which city experienced the minimum PM2.5 levels?",Aizawl 4730,5911,spatial_aggregation,Which state had the lowest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""state""]) true_code() ","Which state had the lowest PM10 concentrations on August 15, 2020?",Meghalaya 4731,5912,spatial_aggregation,Which station had the lowest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""station""]) true_code() ","On August 15, 2020, which station recorded the minimum PM10 levels?","Lumpyngngad, Shillong - Meghalaya PCB" 4732,5914,spatial_aggregation,Which station had the 3rd highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""station""]) true_code() ","On August 15, 2020, which station showed the third-highest PM2.5 concentrations?","Sector 30, Faridabad - HSPCB" 4733,5916,spatial_aggregation,Which state had the lowest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","On August 15, 2021, which state experienced the minimum PM2.5 levels?",Mizoram 4734,5917,spatial_aggregation,Which state had the 2nd lowest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""state""]) true_code() ","Identify the state that had the second-lowest PM10 concentrations on August 15, 2024.",Meghalaya 4735,5918,spatial_aggregation,Which city had the 3rd highest PM10 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On August 15, 2018, which city recorded the third-highest PM10 levels?",Bhiwadi 4736,5919,spatial_aggregation,Which state had the lowest PM2.5 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""state""]) true_code() ","Which state showed the lowest PM2.5 levels on August 15, 2023?",Sikkim 4737,5920,spatial_aggregation,Which city had the 3rd highest PM2.5 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","On August 15, 2019, which city registered the third-highest PM2.5 concentrations?",Jodhpur 4738,5921,spatial_aggregation,Which city had the 3rd highest PM2.5 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""city""]) true_code() ","Identify the city with the third-highest PM2.5 levels on August 15, 2024.",Jalna 4739,5922,spatial_aggregation,Which city had the 3rd highest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""city""]) true_code() ","On August 15, 2019, which city experienced the third-highest PM10 levels?",Bhiwadi 4740,5923,spatial_aggregation,Which station had the lowest PM2.5 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""station""]) true_code() ","Which station had the minimum PM2.5 concentrations on August 15, 2019?","Shrivastav Colony, Damoh - MPPCB" 4741,5924,spatial_aggregation,Which city had the highest PM2.5 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""city""]) true_code() ","On August 15, 2018, which city recorded the highest PM2.5 levels?",Lucknow 4742,5926,spatial_aggregation,Which station had the highest PM2.5 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""station""]) true_code() ","On August 15, 2018, which station registered the highest PM2.5 levels?","Talkatora District Industries Center, Lucknow - CPCB" 4743,5927,spatial_aggregation,Which city had the lowest PM10 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""city""]) true_code() ","Which city experienced the minimum PM10 concentrations on August 15, 2024?",Ooty 4744,5928,spatial_aggregation,Which station had the 2nd lowest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""station""]) true_code() ","On August 15, 2020, which station had the second-lowest PM10 levels?","Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 4745,5931,spatial_aggregation,Which station had the highest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""station""]) true_code() ","Which station showed the highest PM10 levels on August 15, 2019?","GVM Corporation, Visakhapatnam - APPCB" 4746,5932,spatial_aggregation,Which station had the 2nd lowest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""station""]) true_code() ","On August 15, 2021, which station registered the second-lowest PM2.5 concentrations?","Diwator Nagar, Koppal - KSPCB" 4747,5933,spatial_aggregation,Which city had the 2nd highest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""city""]) true_code() ","Identify the city that experienced the second-highest PM10 levels on August 15, 2021.",Moradabad 4748,5935,spatial_aggregation,Which city had the 3rd lowest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""city""]) true_code() ","Which city recorded the third-lowest PM10 concentrations on August 15, 2022?",Damoh 4749,5937,spatial_aggregation,Which station had the 3rd lowest PM2.5 levels on August 15 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2023)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""station""]) true_code() ","Identify the station with the third-lowest PM2.5 levels on August 15, 2023.","Murthal, Sonipat - HSPCB" 4750,5938,spatial_aggregation,Which city had the 2nd lowest PM10 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""city""]) true_code() ","On August 15, 2021, which city registered the second-lowest PM10 concentrations?",Aizawl 4751,5939,spatial_aggregation,Which state had the 3rd highest PM2.5 levels on August 15 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2021)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","Which state experienced the third-highest PM2.5 levels on August 15, 2021?",Chandigarh 4752,5941,spatial_aggregation,Which state had the 3rd highest PM10 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""state""]) true_code() ","Identify the state that recorded the third-highest PM10 concentrations on August 15, 2018.",Jharkhand 4753,5942,spatial_aggregation,Which state had the 3rd highest PM2.5 levels on August 15 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2018)] data = data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""state""]) true_code() ","On August 15, 2018, which state showed the third-highest PM2.5 levels?",Haryana 4754,5944,spatial_aggregation,Which station had the 3rd lowest PM10 levels on August 15 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2022)] data = data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""station""]) true_code() ","On August 15, 2022, which station experienced the third-lowest PM10 concentrations?","Sahilara, Maihar - KJS Cements" 4755,5945,spatial_aggregation,Which state had the 2nd highest PM10 levels on August 15 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2019)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""state""]) true_code() ","Identify the state with the second-highest PM10 levels on August 15, 2019.",Andhra Pradesh 4756,5946,spatial_aggregation,Which city had the highest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""city""]) true_code() ","On August 15, 2020, which city had the highest PM10 levels?",Ballabgarh 4757,5947,spatial_aggregation,Which station had the 2nd highest PM2.5 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""station""]) true_code() ","Which station recorded the second-highest PM2.5 concentrations on August 15, 2020?","Sector-51, Gurugram - HSPCB" 4758,5948,spatial_aggregation,Which city had the 2nd highest PM2.5 levels on August 15 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2024)] data = data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""city""]) true_code() ","On August 15, 2024, which city showed the second-highest PM2.5 levels?",Byrnihat 4759,5949,spatial_aggregation,Which state had the 3rd lowest PM10 levels on August 15 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 15) & (main_data[""Timestamp""].dt.month == 8) & (main_data[""Timestamp""].dt.year == 2020)] data = data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""state""]) true_code() ","Identify the state that registered the third-lowest PM10 levels on August 15, 2020.",Jharkhand 4760,5950,spatial_aggregation,Which station recorded the 3rd lowest average PM10 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd minimum average PM10 level?,"Zero Point GICI, Gangtok - SSPCB" 4761,5951,spatial_aggregation,Which city recorded the highest average PM10 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the maximum average PM10 level?,Panchkula 4762,5952,spatial_aggregation,Which station recorded the lowest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum average PM2.5 level?,"Sikulpuikawn, Aizawl - Mizoram PCB" 4763,5953,spatial_aggregation,Which station recorded the 2nd lowest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd minimum average PM2.5 level?,"Zero Point GICI, Gangtok - SSPCB" 4764,5954,spatial_aggregation,Which city recorded the 2nd highest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum average PM2.5 level?,Byrnihat 4765,5956,spatial_aggregation,Which city recorded the 3rd highest average PM10 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd maximum average PM10 level?,Sri Ganganagar 4766,5957,spatial_aggregation,Which city recorded the highest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the maximum average PM2.5 level?,Thoothukudi 4767,5958,spatial_aggregation,Which state recorded the 3rd highest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest average PM2.5 level?,Haryana 4768,5960,spatial_aggregation,Which state recorded the 3rd highest average PM10 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum average PM10 level?,Haryana 4769,5961,spatial_aggregation,Which station recorded the 3rd lowest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest average PM2.5 level?,"DM College of Science, Imphal - Manipur PCB" 4770,5962,spatial_aggregation,Which state recorded the highest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the maximum average PM2.5 level?,Delhi 4771,5963,spatial_aggregation,Which state recorded the 2nd highest average PM2.5 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum average PM2.5 level?,Bihar 4772,5964,spatial_aggregation,Which station recorded the 2nd highest average PM10 level?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest average PM10 level?,"Sector-6, Panchkula - HSPCB" 4773,5965,spatial_aggregation,How many state have only one station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")['station'].nunique().reset_index() data = data[data['station'] == 1] count = len(data[""state""].to_list()) print(count) true_code() ",What is the number of states having only a single station?,7 4774,5966,spatial_aggregation,How many city have only eight station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")['station'].nunique().reset_index() data = data[data['station'] == 8] count = len(data[""city""].to_list()) print(count) true_code() ",What quantity of cities possess only eight stations?,1 4775,5967,spatial_aggregation,How many city have only six station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")['station'].nunique().reset_index() data = data[data['station'] == 6] count = len(data[""city""].to_list()) print(count) true_code() ",What is the count of cities having only six stations?,4 4776,5968,spatial_aggregation,How many state have only two station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")['station'].nunique().reset_index() data = data[data['station'] == 2] count = len(data[""state""].to_list()) print(count) true_code() ",What number of states possess only two stations?,3 4777,5969,spatial_aggregation,How many state have only nine station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")['station'].nunique().reset_index() data = data[data['station'] == 9] count = len(data[""state""].to_list()) print(count) true_code() ",What is the quantity of states having only nine stations?,2 4778,5970,spatial_aggregation,How many state have only six station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")['station'].nunique().reset_index() data = data[data['station'] == 6] count = len(data[""state""].to_list()) print(count) true_code() ",What is the count of states possessing only six stations?,0 4779,5972,spatial_aggregation,How many state have only four station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")['station'].nunique().reset_index() data = data[data['station'] == 4] count = len(data[""state""].to_list()) print(count) true_code() ",What is the quantity of states having only four stations?,1 4780,5973,spatial_aggregation,How many city have only nine station ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")['station'].nunique().reset_index() data = data[data['station'] == 9] count = len(data[""city""].to_list()) print(count) true_code() ",What is the count of cities possessing only nine stations?,2 4781,5974,spatial_aggregation,Which city has the 2nd lowest number of monitoring stations?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""station""].nunique().reset_index() data = data.sort_values(by=""station"") print(data.iloc[1][""city""]) true_code() ",Which city possesses the 2nd smallest number of monitoring stations?,Maihar 4782,5975,spatial_aggregation,Which city has the 3rd lowest number of monitoring stations?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""station""].nunique().reset_index() data = data.sort_values(by=""station"") print(data.iloc[2][""city""]) true_code() ",Which city has the 3rd minimum count of monitoring stations?,Malegaon 4783,5976,spatial_aggregation,Which state has the highest number of monitoring stations?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""station""].nunique().reset_index() data = data.sort_values(by=""station"") print(data.iloc[-1][""state""]) true_code() ",Which state possesses the highest number of monitoring stations?,Maharashtra 4784,5977,spatial_aggregation,Which city has the 3rd highest number of monitoring stations?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""station""].nunique().reset_index() data = data.sort_values(by=""station"") print(data.iloc[-3][""city""]) true_code() ",Which city has the 3rd largest count of monitoring stations?,Bengaluru 4785,5978,spatial_aggregation,Which state has the lowest number of monitoring stations?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""station""].nunique().reset_index() data = data.sort_values(by=""station"") print(data.iloc[0][""state""]) true_code() ",Which state possesses the smallest number of monitoring stations?,Arunachal Pradesh 4786,5982,spatial_aggregation,Which station recorded the 2nd lowest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second most minimal stable PM2.5 level?,"Vijay Nagar Scheme-78, Indore - Glenmark" 4787,5983,spatial_aggregation,Which city recorded the 2nd highest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest stable PM2.5 level.,Begusarai 4788,5984,spatial_aggregation,Which station recorded the 2nd lowest stablePM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].std().reset_index() data = data.dropna(subset='PM10') data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station documented the second most minimal stable PM10 level.,"Ibrahimpur, Vijayapura - KSPCB" 4789,5985,spatial_aggregation,Which city recorded the 3rd highest stablePM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].std().reset_index() data = data.dropna(subset='PM10') data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the third highest stable PM10 level.,Katihar 4790,5986,spatial_aggregation,Which city recorded the highest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the maximum stable PM2.5 level?,Kolar 4791,5987,spatial_aggregation,Which state recorded the 3rd highest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest stable PM2.5 level.,Uttar Pradesh 4792,5990,spatial_aggregation,Which station recorded the 3rd lowest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station showed the third most minimal stable PM2.5 level?,"Parisutham Nagar, Thanjavur - TNPCB" 4793,5991,spatial_aggregation,Which state recorded the highest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the maximum stable PM2.5 level.,Delhi 4794,5992,spatial_aggregation,Which state recorded the 2nd highest stablePM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].std().reset_index() data = data.dropna(subset='PM2.5') data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state documented the second highest stable PM2.5 level.,Manipur 4795,5993,spatial_aggregation,Which station recorded the 2nd highest stablePM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].std().reset_index() data = data.dropna(subset='PM10') data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the second highest stable PM10 level.,"DRCC Anandpur, Begusarai - BSPCB" 4796,5995,spatial_aggregation,Which state had the highest PM2.5 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ","Report which state showed the maximum PM2.5 level on January 27, 2024.",Haryana 4797,5997,spatial_aggregation,Which station had the highest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","On January 27, 2022, which station documented the peak PM2.5 level?","ITO, Delhi - CPCB" 4798,5998,spatial_aggregation,Which city had the 3rd highest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ","Identify the city that recorded the third highest PM2.5 level on January 27, 2021.",Noida 4799,5999,spatial_aggregation,Which station had the 2nd highest PM2.5 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the second highest PM2.5 level on 27 January 2019.,"Nehru Nagar, Delhi - DPCC" 4800,6000,spatial_aggregation,Which station had the 3rd highest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","Determine the station showing the third highest PM2.5 level on January 27, 2021.","Sector-116, Noida - UPPCB" 4801,6002,spatial_aggregation,Which state had the highest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","Identify the state with the maximum PM10 level on January 27, 2020.",Delhi 4802,6003,spatial_aggregation,Which state had the 3rd highest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state recorded the third highest PM2.5 level on 27 January 2020.,Delhi 4803,6004,spatial_aggregation,Which state had the 2nd lowest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","Determine the state that showed the second most minimal PM2.5 level on January 27, 2022.",Mizoram 4804,6005,spatial_aggregation,Which station had the 3rd lowest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","On January 27, 2019, which station registered the third lowest PM10 level?","Bollaram Industrial Area, Hyderabad - TSPCB" 4805,6006,spatial_aggregation,Which station had the 2nd lowest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that documented the second most minimal PM2.5 level on 27 January 2020.,"Bandhavgar Colony, Satna - Birla Cement" 4806,6007,spatial_aggregation,Which city had the lowest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","Report which city had the minimum PM2.5 level on January 27, 2023.",Sagar 4807,6008,spatial_aggregation,Which state had the 2nd lowest PM10 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state with the second most minimal PM10 level on 27 January 2023.,Jammu and Kashmir 4808,6009,spatial_aggregation,Which city had the 2nd lowest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","On January 27, 2019, which city showed the second lowest PM10 level?",Amaravati 4809,6010,spatial_aggregation,Which station had the 3rd highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third highest PM2.5 level on 27 January 2018.,"Sanjay Palace, Agra - UPPCB" 4810,6012,spatial_aggregation,Which city had the 2nd highest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city showing the second highest PM2.5 level on 27 January 2021.,Greater Noida 4811,6013,spatial_aggregation,Which state had the 2nd highest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","On January 27, 2021, which state had the second highest PM10 level?",Delhi 4812,6014,spatial_aggregation,Which city had the highest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city with the maximum PM2.5 level on 27 January 2020.,Noida 4813,6015,spatial_aggregation,Which state had the 2nd highest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","Report which state documented the second highest PM2.5 level on January 27, 2023.",Karnataka 4814,6017,spatial_aggregation,Which city had the 3rd highest PM10 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","On January 27, 2023, which city showed the third highest PM10 level?",Nalbari 4815,6018,spatial_aggregation,Which state had the 3rd highest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest PM2.5 level on 27 January 2023.,Tripura 4816,6019,spatial_aggregation,Which station had the lowest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","Report which station had the minimum PM2.5 level on January 27, 2022.","Manali Village, Chennai - TNPCB" 4817,6021,spatial_aggregation,Which station had the 3rd lowest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","On January 27, 2020, which station documented the third lowest PM10 level?","Sahilara, Maihar - KJS Cements" 4818,6022,spatial_aggregation,Which state had the highest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","Identify the state that recorded the maximum PM10 level on January 27, 2021.",Punjab 4819,6024,spatial_aggregation,Which city had the 2nd lowest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","Determine the city showing the second most minimal PM2.5 level on January 27, 2018.",Chandrapur 4820,6025,spatial_aggregation,Which city had the highest PM10 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ","On January 27, 2023, which city had the peak PM10 level?",Prayagraj 4821,6027,spatial_aggregation,Which state had the lowest PM10 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","Report which state documented the minimum PM10 level on January 27, 2023.",Karnataka 4822,6028,spatial_aggregation,Which station had the 3rd lowest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station that recorded the third most minimal PM2.5 level on 27 January 2021.,"Bandra, Mumbai - MPCB" 4823,6029,spatial_aggregation,Which state had the 2nd highest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ","On January 27, 2022, which state showed the second highest PM10 level?",Bihar 4824,6030,spatial_aggregation,Which city had the 2nd highest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest PM10 level on 27 January 2022.,Muzaffarpur 4825,6031,spatial_aggregation,Which state had the 2nd highest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ","Report which state had the second highest PM2.5 level on January 27, 2022.",Delhi 4826,6032,spatial_aggregation,Which station had the 3rd highest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station with the third highest PM10 level on 27 January 2020.,"Lajpat Nagar, Moradabad - UPPCB" 4827,6033,spatial_aggregation,Which station had the 2nd highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","On January 27, 2018, which station documented the second highest PM2.5 level?","Vasundhara, Ghaziabad - UPPCB" 4828,6034,spatial_aggregation,Which state had the 2nd lowest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","Identify the state that recorded the second most minimal PM10 level on January 27, 2022.",Madhya Pradesh 4829,6035,spatial_aggregation,Which city had the 3rd lowest PM2.5 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city registered the third lowest PM2.5 level on 27 January 2024.,Mandikhera 4830,6036,spatial_aggregation,Which city had the lowest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","Determine the city showing the minimum PM2.5 level on January 27, 2020.",Eloor 4831,6037,spatial_aggregation,Which city had the 2nd lowest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ","On January 27, 2021, which city had the second most minimal PM2.5 level?",Bagalkot 4832,6041,spatial_aggregation,Which state had the 2nd lowest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","On January 27, 2021, which state showed the second lowest PM10 level?",Maharashtra 4833,6042,spatial_aggregation,Which city had the 2nd lowest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that registered the second most minimal PM2.5 level on 27 January 2020.,Satna 4834,6043,spatial_aggregation,Which station had the highest PM10 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ","Report which station had the peak PM10 level on January 27, 2018.","Anand Vihar, Delhi - DPCC" 4835,6044,spatial_aggregation,Which state had the 3rd highest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state with the third highest PM10 level on 27 January 2021.,Delhi 4836,6045,spatial_aggregation,Which city had the 3rd lowest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","On January 27, 2021, which city documented the third lowest PM10 level?",Bagalkot 4837,6046,spatial_aggregation,Which state had the 3rd lowest PM10 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","Identify the state that recorded the third most minimal PM10 level on January 27, 2024.",Karnataka 4838,6047,spatial_aggregation,Which station had the 3rd highest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report which station registered the third highest PM10 level on 27 January 2019.,"Dwarka-Sector 8, Delhi - DPCC" 4839,6048,spatial_aggregation,Which station had the 3rd lowest PM2.5 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","Determine the station showing the third lowest PM2.5 level on January 27, 2024.","General Hospital, Mandikhera - HSPCB" 4840,6049,spatial_aggregation,Which city had the 2nd lowest PM10 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","On January 27, 2018, which city had the second most minimal PM10 level?",Varanasi 4841,6050,spatial_aggregation,Which city had the 2nd lowest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city with the second lowest PM2.5 level on 27 January 2022.,Aizawl 4842,6052,spatial_aggregation,Which station had the 2nd highest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the second highest PM10 level on 27 January 2022.,"MIT-Daudpur Kothi, Muzaffarpur - BSPCB" 4843,6053,spatial_aggregation,Which station had the lowest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ","On January 27, 2022, which station showed the minimum PM10 level?","Royapuram, Chennai - TNPCB" 4844,6054,spatial_aggregation,Which station had the highest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the maximum PM2.5 level on 27 January 2020.,"Sector-116, Noida - UPPCB" 4845,6056,spatial_aggregation,Which city had the highest PM10 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city with the peak PM10 level on 27 January 2024.,Kishanganj 4846,6057,spatial_aggregation,Which city had the lowest PM2.5 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ","On January 27, 2022, which city documented the minimum PM2.5 level?",Chennai 4847,6058,spatial_aggregation,Which state had the lowest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ","Identify the state that recorded the minimum PM10 level on January 27, 2020.",Karnataka 4848,6060,spatial_aggregation,Which station had the lowest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","Determine the station showing the minimum PM2.5 level on January 27, 2018.","BWSSB Kadabesanahalli, Bengaluru - CPCB" 4849,6061,spatial_aggregation,Which station had the 3rd highest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ","On January 27, 2020, which station had the third highest PM2.5 level?","Wazirpur, Delhi - DPCC" 4850,6062,spatial_aggregation,Which city had the 3rd highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city with the third highest PM2.5 level on 27 January 2018.,Agra 4851,6064,spatial_aggregation,Which state had the 2nd lowest PM10 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the second most minimal PM10 level on 27 January 2024.,Meghalaya 4852,6066,spatial_aggregation,Which state had the lowest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that registered the minimum PM2.5 level on 27 January 2023.,Madhya Pradesh 4853,6068,spatial_aggregation,Which city had the 3rd highest PM2.5 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city with the third highest PM2.5 level on 27 January 2024.,Hanumangarh 4854,6069,spatial_aggregation,Which city had the 3rd highest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ","On January 27, 2019, which city documented the third highest PM10 level?",Delhi 4855,6070,spatial_aggregation,Which station had the lowest PM2.5 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ","Identify the station that recorded the minimum PM2.5 level on January 27, 2019.","Bandhavgar Colony, Satna - Birla Cement" 4856,6071,spatial_aggregation,Which city had the highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city registered the maximum PM2.5 level on 27 January 2018.,Faridabad 4857,6073,spatial_aggregation,Which station had the highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ","On January 27, 2018, which station had the peak PM2.5 level?","Sector- 16A, Faridabad - HSPCB" 4858,6075,spatial_aggregation,Which station had the 2nd lowest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ","Report which station documented the second most minimal PM10 level on January 27, 2020.","Airoli, Navi Mumbai - MPCB" 4859,6076,spatial_aggregation,Which state had the highest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that recorded the maximum PM2.5 level on 27 January 2023.,Karnataka 4860,6077,spatial_aggregation,Which state had the 2nd lowest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ","On January 27, 2018, which state showed the second lowest PM2.5 level?",Maharashtra 4861,6078,spatial_aggregation,Which station had the highest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the peak PM10 level on 27 January 2019.,"RIICO Ind. Area III, Bhiwadi - RSPCB" 4862,6079,spatial_aggregation,Which station had the 2nd lowest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ","Report which station had the second most minimal PM2.5 level on January 27, 2021.","Vidayagiri, Bagalkot - KSPCB" 4863,6081,spatial_aggregation,Which state had the 2nd lowest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ","On January 27, 2020, which state documented the second lowest PM10 level?",Maharashtra 4864,6082,spatial_aggregation,Which city had the 3rd lowest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ","Identify the city that recorded the third most minimal PM10 level on January 27, 2022.",Chennai 4865,6083,spatial_aggregation,Which city had the 3rd lowest PM2.5 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city registered the third lowest PM2.5 level on 27 January 2019.,Chennai 4866,6084,spatial_aggregation,Which station had the 3rd lowest PM2.5 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ","Determine the station showing the third lowest PM2.5 level on January 27, 2023.","Rajbagh, Srinagar - JKSPCB" 4867,6085,spatial_aggregation,Which city had the 2nd lowest PM10 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ","On January 27, 2021, which city had the second most minimal PM10 level?",Navi Mumbai 4868,6086,spatial_aggregation,Which state had the 3rd highest PM2.5 level on 27 January 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2021)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state with the third highest PM2.5 level on 27 January 2021.,Uttar Pradesh 4869,6087,spatial_aggregation,Which state had the highest PM10 level on 27 January 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2023)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ","Report which state documented the maximum PM10 level on January 27, 2023.",Uttar Pradesh 4870,6088,spatial_aggregation,Which state had the 3rd highest PM10 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that recorded the third highest PM10 level on 27 January 2018.,Delhi 4871,6089,spatial_aggregation,Which state had the 3rd highest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ","On January 27, 2018, which state showed the third highest PM2.5 level?",Uttar Pradesh 4872,6090,spatial_aggregation,Which state had the 3rd lowest PM2.5 level on 27 January 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2018)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third most minimal PM2.5 level on 27 January 2018.,Maharashtra 4873,6091,spatial_aggregation,Which station had the 3rd lowest PM10 level on 27 January 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2022)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ","Report which station had the third lowest PM10 level on January 27, 2022.","Velachery Res. Area, Chennai - CPCB" 4874,6092,spatial_aggregation,Which state had the 2nd highest PM10 level on 27 January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2019)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state with the second highest PM10 level on 27 January 2019.,Delhi 4875,6094,spatial_aggregation,Which station had the 2nd highest PM2.5 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ","Identify the station that recorded the second highest PM2.5 level on January 27, 2020.","Loni, Ghaziabad - UPPCB" 4876,6095,spatial_aggregation,Which city had the 2nd highest PM2.5 level on 27 January 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2024)] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the second highest PM2.5 level on 27 January 2024.,Delhi 4877,6096,spatial_aggregation,Which state had the 3rd lowest PM10 level on 27 January 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.day == 27) & (main_data[""Timestamp""].dt.month == 1) & (main_data[""Timestamp""].dt.year == 2020)] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ","Determine the state showing the third most minimal PM10 level on January 27, 2020.",Madhya Pradesh 4878,6097,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2021, which station showed the second smallest decrease in its 25th percentile PM10 levels comparing December to October?","New Industrial Town, Faridabad - HSPCB" 4879,6098,spatio_temporal_aggregation,Which city experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing October to December in 2021, which city experienced the smallest reduction in its 25th percentile PM2.5 levels?",Muzaffarpur 4880,6099,spatio_temporal_aggregation,Which station experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station demonstrated the largest drop in its 25th percentile PM2.5 levels between October and December of 2020?,"Mini Secretariat, Charkhi Dadri - HSPCB" 4881,6100,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2020, which city ranked third for the smallest decrease in 25th percentile PM2.5 levels from October to December?",Patna 4882,6101,spatio_temporal_aggregation,Which city experienced the highest median PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant fall in median PM10 levels when comparing December 2018 to October 2018.,Chandrapur 4883,6102,spatio_temporal_aggregation,Which city experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which city experienced the least significant drop in its 75th percentile PM10 levels between October and December 2020?,Durgapur 4884,6103,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2021, which city had the smallest decrease in average PM10 levels?",Muzaffarpur 4885,6104,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2024, which city will rank third for the smallest reduction in median PM2.5 levels from October to December?",Durgapur 4886,6105,spatio_temporal_aggregation,Which state experienced the lowest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2021 to October 2021, which state showed the least significant drop in median PM10 levels?",Bihar 4887,6106,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state exhibited the second largest decrease in its 25th percentile PM2.5 levels between October and December of 2021?,Jammu and Kashmir 4888,6107,spatio_temporal_aggregation,Which station experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2020, which station ranked third for the most substantial fall in average PM2.5 levels from October to December?","Vidayagiri, Bagalkot - KSPCB" 4889,6108,spatio_temporal_aggregation,Which city experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that experienced the highest drop in 75th percentile PM10 levels when comparing December 2019 to October 2019.,Varanasi 4890,6109,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2018, which station had the third smallest decrease in median PM2.5 levels?","Muzaffarpur Collectorate, Muzaffarpur - BSPCB" 4891,6110,spatio_temporal_aggregation,Which city experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city demonstrated the third largest reduction in its average PM2.5 levels between October and December of 2020?,Bagalkot 4892,6111,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2023, which state showed the second smallest decrease in its 75th percentile PM2.5 levels comparing December to October?",Himachal Pradesh 4893,6112,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing October to December in 2023, which city experienced the second smallest reduction in its 75th percentile PM10 levels?",Chhapra 4894,6113,spatio_temporal_aggregation,Which station experienced the highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station demonstrated the largest drop in its median PM10 levels between October and December of 2019?,"Ardhali Bazar, Varanasi - UPPCB" 4895,6114,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2018, which station ranked with the smallest decrease in 25th percentile PM10 levels from October to December?","Talcher Coalfields,Talcher - OSPCB" 4896,6116,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its median PM10 levels between October and December 2022?,Chhattisgarh 4897,6117,spatio_temporal_aggregation,Which station experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2020, which station had the smallest decrease in 75th percentile PM2.5 levels?","Muzaffarpur Collectorate, Muzaffarpur - BSPCB" 4898,6118,spatio_temporal_aggregation,Which city experienced the 2nd lowest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2022, which city will rank second for the smallest reduction in median PM10 levels from October to December?",Chhapra 4899,6119,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2022 to October 2022, which state showed the third least significant drop in median PM10 levels?",Assam 4900,6120,spatio_temporal_aggregation,Which state experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which state exhibited the second smallest decrease in its median PM2.5 levels between October and December of 2021?,Bihar 4901,6121,spatio_temporal_aggregation,Which city experienced the 2nd lowest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which station ranked second for the smallest decrease in median PM10 levels from October to December?",Ballabgarh 4902,6122,spatio_temporal_aggregation,Which state experienced the lowest median PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in median PM2.5 levels when comparing December 2018 to October 2018.,Bihar 4903,6123,spatio_temporal_aggregation,Which city experienced the highest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city experienced the most significant drop in its median PM10 levels between October and December 2021?,Moradabad 4904,6124,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2019, which state had the smallest decrease in 25th percentile PM2.5 levels?",Assam 4905,6125,spatio_temporal_aggregation,Which city experienced the highest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2024, which city will rank with the largest reduction in average PM10 levels from October to December?",Hapur 4906,6127,spatio_temporal_aggregation,Which station experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station exhibited the largest decrease in its 25th percentile PM10 levels between October and December of 2022?,"Deen Dayal Nagar, Sagar - MPPCB" 4907,6128,spatio_temporal_aggregation,Which station experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2023, which station ranked third for the most substantial fall in 75th percentile PM10 levels from October to December?","Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 4908,6129,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the state that experienced the second highest drop in average PM10 levels when comparing December 2021 to October 2021.,Chhattisgarh 4909,6130,spatio_temporal_aggregation,Which state experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2023, which state had the second smallest decrease in average PM2.5 levels?",Himachal Pradesh 4910,6131,spatio_temporal_aggregation,Which station experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station demonstrated the second largest reduction in its average PM2.5 levels between October and December of 2020?,"GIDC, Nandesari - Nandesari Ind. Association" 4911,6132,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2023, which state showed the second largest decrease in its average PM2.5 levels comparing December to October?",Mizoram 4912,6133,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing October to December in 2018, which station experienced the second smallest reduction in its 75th percentile PM2.5 levels?","Muzaffarpur Collectorate, Muzaffarpur - BSPCB" 4913,6134,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which station demonstrated the third smallest drop in its median PM10 levels between October and December of 2021?,West Bengal 4914,6135,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which state ranked with the second smallest decrease in 75th percentile PM10 levels from October to December?",Jharkhand 4915,6136,spatio_temporal_aggregation,Which state experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Identify the state that saw the third most significant fall in 25th percentile PM10 levels when comparing December 2022 to October 2022.,Puducherry 4916,6137,spatio_temporal_aggregation,Which city experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city experienced the third most significant drop in its 75th percentile PM10 levels between October and December 2022?,Bulandshahr 4917,6138,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2023, which city had the third smallest decrease in 25th percentile PM2.5 levels?",Bhagalpur 4918,6139,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2023, which state will rank with the smallest reduction in 75th percentile PM10 levels from October to December?",Himachal Pradesh 4919,6142,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2021, which state ranked with the smallest decrease in 25th percentile PM2.5 levels from October to December?",Delhi 4920,6143,spatio_temporal_aggregation,Which city experienced the highest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant fall in median PM10 levels when comparing December 2022 to October 2022.,Nandesari 4921,6144,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which station experienced the second least significant drop in its 25th percentile PM2.5 levels between October and December 2024?,"PCBL Residential Complex, Durgapur - WBPCB" 4922,6147,spatio_temporal_aggregation,Which city experienced the lowest median PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2019 to October 2019, which city showed the least significant drop in median PM2.5 levels?",Noida 4923,6149,spatio_temporal_aggregation,Which state experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2018, which state ranked with the second largest decrease in 75th percentile PM2.5 levels from October to December?",Punjab 4924,6150,spatio_temporal_aggregation,Which city experienced the lowest median PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the city that saw the least significant fall in median PM2.5 levels when comparing December 2020 to October 2020.,Kanpur 4925,6151,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its 25th percentile PM10 levels between October and December 2024?,Begusarai 4926,6152,spatio_temporal_aggregation,Which station experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2022, which station had the second largest decrease in median PM2.5 levels?","Paryavaran Parisar, Bhopal - MPPCB" 4927,6154,spatio_temporal_aggregation,Which state experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2024 to October 2024, which state showed the most significant drop in 25th percentile PM10 levels?",Uttarakhand 4928,6155,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state exhibited the largest decrease in its 75th percentile PM2.5 levels between October and December of 2023?,Gujarat 4929,6156,spatio_temporal_aggregation,Which state experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2018, which state ranked with the largest decrease in 25th percentile PM10 levels from October to December?",Haryana 4930,6157,spatio_temporal_aggregation,Which station experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the station that saw the third least significant fall in 25th percentile PM10 levels when comparing December 2024 to October 2024.,"Govt. High School Shikarpur, Patna - BSPCB" 4931,6158,spatio_temporal_aggregation,Which city experienced the 2nd highest median PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city experienced the second most significant drop in its median PM10 levels between October and December 2020?,Hapur 4932,6159,spatio_temporal_aggregation,Which state experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2020, which state had the largest decrease in 25th percentile PM2.5 levels?",Punjab 4933,6160,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2024, which station will rank second for the smallest reduction in median PM10 levels from October to December?","Trivenidevi Bhalotia College, Asansol - WBPCB" 4934,6161,spatio_temporal_aggregation,Which station experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2024 to October 2024, which station showed the third most significant drop in 75th percentile PM2.5 levels?","CRRI Mathura Road, Delhi - IMD" 4935,6162,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city exhibited the third largest decrease in its 25th percentile PM10 levels between October and December of 2019?,Sonipat 4936,6163,spatio_temporal_aggregation,Which city experienced the 2nd highest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2024, which city ranked with the second largest decrease in median PM10 levels from October to December?",Hapur 4937,6165,spatio_temporal_aggregation,Which city experienced the 3rd highest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city experienced the third most significant drop in its average PM10 levels between October and December 2024?,Ghaziabad 4938,6168,spatio_temporal_aggregation,Which state experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2023 to October 2023, which state showed the third least significant drop in 25th percentile PM10 levels?",Himachal Pradesh 4939,6169,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city exhibited the second largest decrease in its average PM10 levels between October and December of 2018?,Mandideep 4940,6171,spatio_temporal_aggregation,Which station experienced the highest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station that saw the most significant fall in median PM10 levels when comparing December 2023 to October 2023.,"Bandhavgar Colony, Satna - Birla Cement" 4941,6172,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its 75th percentile PM2.5 levels between October and December 2021?,Patna 4942,6174,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which station will rank second for the smallest reduction in 75th percentile PM10 levels from October to December?","Nathu Colony, Ballabgarh - HSPCB" 4943,6175,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2020 to October 2020, which state showed the most significant drop in 75th percentile PM10 levels?",Haryana 4944,6176,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which state exhibited the third smallest decrease in its 75th percentile PM10 levels between October and December of 2022?,Saharsa 4945,6177,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2023, which station ranked with the second smallest decrease in average PM2.5 levels from October to December?","Nehru Nagar, Delhi - DPCC" 4946,6178,spatio_temporal_aggregation,Which city experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant fall in 75th percentile PM2.5 levels when comparing December 2021 to October 2021.,Gummidipoondi 4947,6179,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its 25th percentile PM2.5 levels between October and December 2020?,Gujarat 4948,6180,spatio_temporal_aggregation,Which state experienced the 2nd lowest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2024, which state had the second smallest decrease in median PM10 levels?",Tripura 4949,6182,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2021 to October 2021, which station showed the second least significant drop in average PM2.5 levels?","Nehru Nagar, Delhi - DPCC" 4950,6183,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station exhibited the second largest decrease in its 25th percentile PM2.5 levels between October and December of 2019?,"Anand Vihar, Hapur - UPPCB" 4951,6184,spatio_temporal_aggregation,Which station experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2018, which station ranked with the third smallest decrease in 25th percentile PM2.5 levels from October to December?","Anand Vihar, Delhi - DPCC" 4952,6185,spatio_temporal_aggregation,Which state experienced the lowest average PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in average PM2.5 levels when comparing December 2022 to October 2022.,Bihar 4953,6186,spatio_temporal_aggregation,Which city experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which city experienced the least significant drop in its 25th percentile PM2.5 levels between October and December 2018?,Patna 4954,6187,spatio_temporal_aggregation,Which city experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2020, which city had the second smallest decrease in 25th percentile PM10 levels?",Kolkata 4955,6188,spatio_temporal_aggregation,Which station experienced the 3rd highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2019, which station will rank third for the largest reduction in median PM10 levels from October to December?","Gobind Pura, Yamuna Nagar - HSPCB" 4956,6189,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2023 to October 2023, which station showed the third most significant drop in 25th percentile PM2.5 levels?","Kokapet, Hyderabad - TSPCB" 4957,6191,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2018, which station ranked with the second largest decrease in 25th percentile PM10 levels from October to December?","Police Commissionerate, Jaipur - RSPCB" 4958,6192,spatio_temporal_aggregation,Which state experienced the lowest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in average PM10 levels when comparing December 2019 to October 2019.,Odisha 4959,6194,spatio_temporal_aggregation,Which state experienced the lowest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2024, which state had the smallest decrease in average PM10 levels?",Jharkhand 4960,6195,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2019, which state will rank with the second largest reduction in average PM10 levels from October to December?",Chandigarh 4961,6196,spatio_temporal_aggregation,Which state experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","Comparing December 2019 to October 2019, which state showed the second most significant drop in 75th percentile PM2.5 levels?",Rajasthan 4962,6198,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2022, which city ranked with the second smallest decrease in average PM2.5 levels from October to December?",Purnia 4963,6199,spatio_temporal_aggregation,Which state experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the state that saw the third least significant fall in 75th percentile PM10 levels when comparing December 2022 to October 2022.,West Bengal 4964,6200,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which city experienced the second least significant drop in its average PM10 levels between October and December 2022?,Saharsa 4965,6201,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2022, which city had the third smallest decrease in average PM10 levels?",Chhapra 4966,6202,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2020, which state will rank with the smallest reduction in 25th percentile PM2.5 levels from October to December?",West Bengal 4967,6203,spatio_temporal_aggregation,Which station experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2019 to October 2019, which station showed the third most significant drop in average PM2.5 levels?","Pallavpuram Phase 2, Meerut - UPPCB" 4968,6204,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which station exhibited the third smallest decrease in its median PM10 levels between October and December of 2023?,"Kareemganj, Gaya - BSPCB" 4969,6205,spatio_temporal_aggregation,Which station experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2021, which station ranked with the smallest decrease in 75th percentile PM2.5 levels from October to December?","Jahangirpuri, Delhi - DPCC" 4970,6207,spatio_temporal_aggregation,Which station experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which station experienced the third most significant drop in its 75th percentile PM10 levels between October and December 2021?,"Manoharpur, Agra - UPPCB" 4971,6209,spatio_temporal_aggregation,Which station experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2023, which station will rank with the largest reduction in 25th percentile PM2.5 levels from October to December?","IIPHG Lekawada, Gandhinagar - IITM" 4972,6210,spatio_temporal_aggregation,Which station experienced the lowest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2021 to October 2021, which station showed the least significant drop in median PM10 levels?","Ghusuri, Howrah - WBPCB" 4973,6211,spatio_temporal_aggregation,Which state experienced the lowest median PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which state exhibited the smallest decrease in its median PM10 levels between October and December of 2018?,Odisha 4974,6212,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2018, which state ranked with the second largest decrease in 25th percentile PM10 levels from October to December?",Madhya Pradesh 4975,6213,spatio_temporal_aggregation,Which station experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the station that saw the second most significant fall in 75th percentile PM2.5 levels when comparing December 2020 to October 2020.,"Vidayagiri, Bagalkot - KSPCB" 4976,6214,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its average PM2.5 levels between October and December 2021?,Manesar 4977,6215,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2018, which state had the smallest decrease in 25th percentile PM10 levels?",Odisha 4978,6216,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2019, which city will rank third for the smallest reduction in average PM10 levels from October to December?",Howrah 4979,6217,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2024 to October 2024, which state showed the third most significant drop in average PM2.5 levels?",Uttar Pradesh 4980,6218,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which station exhibited the third smallest decrease in its median PM10 levels between October and December of 2019?,"Nehru Nagar, Delhi - DPCC" 4981,6219,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2022, which city ranked with the third largest decrease in median PM2.5 levels from October to December?",Anantapur 4982,6220,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the station that saw the second least significant fall in 25th percentile PM2.5 levels when comparing December 2019 to October 2019.,"Nehru Nagar, Delhi - DPCC" 4983,6222,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","For the period October to December 2020, which city had the third largest decrease in 25th percentile PM10 levels?",Sirsa 4984,6223,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2018, which state will rank with the third smallest reduction in average PM2.5 levels from October to December?",Uttar Pradesh 4985,6224,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2022 to October 2022, which station showed the third most significant drop in 25th percentile PM10 levels?","Anand Vihar, Delhi - DPCC" 4986,6225,spatio_temporal_aggregation,Which city experienced the 2nd highest 75th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city exhibited the second largest decrease in its 75th percentile PM10 levels between October and December of 2024?,Hapur 4987,6226,spatio_temporal_aggregation,Which state experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2024, which state ranked with the third largest decrease in 75th percentile PM2.5 levels from October to December?",Uttar Pradesh 4988,6227,spatio_temporal_aggregation,Which station experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the station that saw the second most significant fall in average PM2.5 levels when comparing December 2021 to October 2021.,"GIDC, Nandesari - Nandesari Ind. Association" 4989,6228,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which city experienced the least significant drop in its average PM10 levels between October and December 2023?,Begusarai 4990,6229,spatio_temporal_aggregation,Which station experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2021, which station had the third smallest decrease in 75th percentile PM2.5 levels?","R K Puram, Delhi - DPCC" 4991,6230,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2023, which city will rank with the second largest reduction in average PM10 levels from October to December?",Jalgaon 4992,6232,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city exhibited the third smallest decrease in its 75th percentile PM2.5 levels between October and December of 2024?,Durgapur 4993,6233,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2024, which state ranked with the largest decrease in 75th percentile PM10 levels from October to December?",Delhi 4994,6234,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Identify the state that saw the third most significant fall in average PM2.5 levels when comparing December 2020 to October 2020.,Karnataka 4995,6235,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its average PM2.5 levels between October and December 2024?,Durgapur 4996,6236,spatio_temporal_aggregation,Which state experienced the highest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2024, which state had the largest decrease in median PM2.5 levels?",Uttar Pradesh 4997,6237,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2022, which state will rank with the second largest reduction in average PM2.5 levels from October to December?",Jammu and Kashmir 4998,6239,spatio_temporal_aggregation,Which state experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which state exhibited the second smallest decrease in its median PM2.5 levels between October and December of 2024?,West Bengal 4999,6240,spatio_temporal_aggregation,Which city experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2018, which city ranked with the second largest decrease in 25th percentile PM2.5 levels from October to December?",Nashik 5000,6241,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the station that saw the second least significant fall in 25th percentile PM2.5 levels when comparing December 2022 to October 2022.,"Mariam Nagar, Purnia - BSPCB" 5001,6242,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which station experienced the second least significant drop in its average PM2.5 levels between October and December 2019?,"IGSC Planetarium Complex, Patna - BSPCB" 5002,6243,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2022, which station had the second largest decrease in 25th percentile PM2.5 levels?","Palayam, Kozhikode - Kerala PCB" 5003,6244,spatio_temporal_aggregation,Which state experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2018, which state will rank with the third smallest reduction in 25th percentile PM10 levels from October to December?",West Bengal 5004,6245,spatio_temporal_aggregation,Which station experienced the 3rd highest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2019 to October 2019, which station showed the third most significant drop in average PM10 levels?","Rishi Nagar, Kaithal - HSPCB" 5005,6247,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2021, which state ranked with the smallest decrease in 75th percentile PM10 levels from October to December?",Bihar 5006,6248,spatio_temporal_aggregation,Which station experienced the 3rd lowest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the station that saw the third least significant fall in average PM10 levels when comparing December 2020 to October 2020.,"Rabindra Bharati University, Kolkata - WBPCB" 5007,6249,spatio_temporal_aggregation,Which station experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station experienced the second most significant drop in its 75th percentile PM2.5 levels between October and December 2021?,"Panchal Nagar, Gadag - KSPCB" 5008,6250,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2020, which state had the second smallest decrease in 75th percentile PM2.5 levels?",Assam 5009,6251,spatio_temporal_aggregation,Which city experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2022, which city will rank second for the smallest reduction in 25th percentile PM2.5 levels from October to December?",Purnia 5010,6252,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2023 to October 2023, which station showed the second least significant drop in 75th percentile PM10 levels?","DRCC Anandpur, Begusarai - BSPCB" 5011,6253,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which station exhibited the second smallest decrease in its median PM10 levels between October and December of 2022?,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 5012,6255,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in 75th percentile PM2.5 levels when comparing December 2022 to October 2022.,Bihar 5013,6256,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its average PM2.5 levels between October and December 2019?,Chandigarh 5014,6257,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2022, which state had the smallest decrease in 75th percentile PM10 levels?",Bihar 5015,6258,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2023, which city will rank third for the smallest reduction in 75th percentile PM2.5 levels from October to December?",Katihar 5016,6259,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2020 to October 2020, which city showed the second least significant drop in 75th percentile PM10 levels?",Guwahati 5017,6261,spatio_temporal_aggregation,Which city experienced the lowest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2019, which city ranked with the smallest decrease in median PM10 levels from October to December?",Talcher 5018,6262,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the station that saw the second least significant fall in median PM2.5 levels when comparing December 2023 to October 2023.,"Nehru Nagar, Delhi - DPCC" 5019,6263,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which city experienced the second least significant drop in its average PM10 levels between October and December 2020?,Howrah 5020,6264,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2020, which city had the third smallest decrease in 25th percentile PM10 levels?",Howrah 5021,6266,spatio_temporal_aggregation,Which state experienced the lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2024 to October 2024, which state showed the least significant drop in median PM2.5 levels?",Tripura 5022,6268,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2019, which city ranked with the third largest decrease in median PM10 levels from October to December?",Yamuna Nagar 5023,6269,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Identify the city that saw the third most significant fall in median PM10 levels when comparing December 2022 to October 2022.,Palwal 5024,6271,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2024, which state had the second largest decrease in 25th percentile PM10 levels?",Himachal Pradesh 5025,6272,spatio_temporal_aggregation,Which city experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2023, which city will rank third for the largest reduction in average PM2.5 levels from October to December?",Mira-Bhayandar 5026,6273,spatio_temporal_aggregation,Which station experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","Comparing December 2020 to October 2020, which station showed the second most significant drop in median PM2.5 levels?","Vidayagiri, Bagalkot - KSPCB" 5027,6274,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city exhibited the third smallest decrease in its median PM2.5 levels between October and December of 2023?,Hanumangarh 5028,6277,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its average PM10 levels between October and December 2021?,Howrah 5029,6278,spatio_temporal_aggregation,Which city experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2021, which city had the second largest decrease in median PM2.5 levels?",Nandesari 5030,6279,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2020, which city will rank with the second largest reduction in average PM10 levels from October to December?",Hapur 5031,6280,spatio_temporal_aggregation,Which city experienced the lowest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2021 to October 2021, which city showed the least significant drop in average PM2.5 levels?",Delhi 5032,6281,spatio_temporal_aggregation,Which city experienced the 3rd highest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city exhibited the third largest decrease in its median PM10 levels between October and December of 2024?,Ghaziabad 5033,6282,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2021, which city ranked with the second largest decrease in average PM10 levels from October to December?",Nandesari 5034,6283,spatio_temporal_aggregation,Which station experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the station that saw the second most significant fall in average PM2.5 levels when comparing December 2019 to October 2019.,"Anand Vihar, Hapur - UPPCB" 5035,6284,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which state experienced the third most significant drop in its median PM2.5 levels between October and December 2024?,Haryana 5036,6286,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which station will rank second for the smallest reduction in 25th percentile PM10 levels from October to December?","Talcher Coalfields,Talcher - OSPCB" 5037,6287,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2024 to October 2024, which state showed the third least significant drop in median PM10 levels?",West Bengal 5038,6288,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station exhibited the second largest decrease in its 25th percentile PM2.5 levels between October and December of 2024?,"Anand Vihar, Hapur - UPPCB" 5039,6289,spatio_temporal_aggregation,Which city experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which city ranked with the second smallest decrease in 25th percentile PM10 levels from October to December?",Ballabgarh 5040,6290,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the city that saw the third least significant fall in median PM10 levels when comparing December 2024 to October 2024.,Sasaram 5041,6291,spatio_temporal_aggregation,Which city experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city experienced the most significant drop in its 75th percentile PM2.5 levels between October and December 2024?,Imphal 5042,6292,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2022, which city had the third smallest decrease in 75th percentile PM2.5 levels?",Bettiah 5043,6294,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2022 to October 2022, which station showed the third least significant drop in median PM2.5 levels?","Kamalnath Nagar, Bettiah - BSPCB" 5044,6295,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which state exhibited the smallest decrease in its 25th percentile PM10 levels between October and December of 2024?,Tripura 5045,6297,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant fall in 25th percentile PM2.5 levels when comparing December 2021 to October 2021.,Gummidipoondi 5046,6298,spatio_temporal_aggregation,Which state experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its 25th percentile PM2.5 levels between October and December 2023?,Jammu and Kashmir 5047,6299,spatio_temporal_aggregation,Which state experienced the highest median PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2018, which state had the largest decrease in median PM10 levels?",Haryana 5048,6300,spatio_temporal_aggregation,Which state experienced the lowest average PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2018, which state will rank with the smallest reduction in average PM2.5 levels from October to December?",Bihar 5049,6301,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2018 to October 2018, which city showed the third least significant drop in 75th percentile PM2.5 levels?",Noida 5050,6302,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which city exhibited the smallest decrease in its average PM10 levels between October and December of 2020?,Durgapur 5051,6303,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2024, which station ranked with the second smallest decrease in average PM10 levels from October to December?","Central Academy for SFS, Byrnihat - PCBA" 5052,6304,spatio_temporal_aggregation,Which state experienced the lowest median PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in median PM2.5 levels when comparing December 2020 to October 2020.,West Bengal 5053,6306,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2019, which station had the second largest decrease in 25th percentile PM10 levels?","Municipal Corporation Office, Dharuhera - HSPCB" 5054,6310,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2018, which state ranked with the third largest decrease in median PM10 levels from October to December?",Telangana 5055,6311,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the state that saw the second least significant fall in 75th percentile PM10 levels when comparing December 2023 to October 2023.,Bihar 5056,6312,spatio_temporal_aggregation,Which station experienced the 3rd highest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which station experienced the third most significant drop in its median PM10 levels between October and December 2023?,"Bandra Kurla Complex, Mumbai - MPCB" 5057,6313,spatio_temporal_aggregation,Which city experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2023, which city had the second smallest decrease in 25th percentile PM2.5 levels?",Saharsa 5058,6314,spatio_temporal_aggregation,Which city experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2022, which city will rank third for the largest reduction in average PM2.5 levels from October to December?",Anantapur 5059,6318,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in 25th percentile PM2.5 levels when comparing December 2024 to October 2024.,Tripura 5060,6319,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which state experienced the least significant drop in its 75th percentile PM2.5 levels between October and December 2024?,West Bengal 5061,6320,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","For the period October to December 2021, which station had the third largest decrease in 25th percentile PM10 levels?","GIDC, Nandesari - Nandesari Ind. Association" 5062,6321,spatio_temporal_aggregation,Which state experienced the lowest median PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2024, which state will rank with the smallest reduction in median PM10 levels from October to December?",Jharkhand 5063,6322,spatio_temporal_aggregation,Which state experienced the highest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2023 to October 2023, which state showed the most significant drop in median PM10 levels?",Gujarat 5064,6323,spatio_temporal_aggregation,Which station experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station exhibited the second largest decrease in its 75th percentile PM2.5 levels between October and December of 2018?,"RIMT University, Mandi Gobindgarh - PPCB" 5065,6324,spatio_temporal_aggregation,Which state experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2018, which state ranked with the largest decrease in 25th percentile PM2.5 levels from October to December?",Gujarat 5066,6325,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Identify the station that saw the third most significant fall in 25th percentile PM2.5 levels when comparing December 2018 to October 2018.,"NSIT Dwarka, Delhi - CPCB" 5067,6327,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2021, which station had the second smallest decrease in 25th percentile PM2.5 levels?","Nehru Nagar, Delhi - DPCC" 5068,6328,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2019, which city will rank third for the smallest reduction in 25th percentile PM10 levels from October to December?",Kalyan 5069,6329,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2020 to October 2020, which city showed the most significant drop in 25th percentile PM2.5 levels?",Charkhi Dadri 5070,6331,spatio_temporal_aggregation,Which city experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2019, which city ranked with the third largest decrease in 75th percentile PM10 levels from October to December?",Sirsa 5071,6332,spatio_temporal_aggregation,Which city experienced the lowest average PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the city that saw the least significant fall in average PM10 levels when comparing December 2022 to October 2022.,Darbhanga 5072,6333,spatio_temporal_aggregation,Which state experienced the highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state experienced the most significant drop in its median PM10 levels between October and December 2019?,Meghalaya 5073,6335,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2019, which state will rank with the second smallest reduction in 75th percentile PM2.5 levels from October to December?",Uttar Pradesh 5074,6338,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2020, which city ranked with the third smallest decrease in median PM2.5 levels from October to December?",Ghaziabad 5075,6339,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that saw the most significant fall in 75th percentile PM2.5 levels when comparing December 2018 to October 2018.,Gujarat 5076,6340,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which state experienced the third least significant drop in its average PM10 levels between October and December 2020?,Bihar 5077,6341,spatio_temporal_aggregation,Which city experienced the 2nd highest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","For the period October to December 2021, which city had the second largest decrease in median PM10 levels?",Gummidipoondi 5078,6342,spatio_temporal_aggregation,Which station experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2024, which station will rank third for the largest reduction in average PM2.5 levels from October to December?","Anand Vihar, Hapur - UPPCB" 5079,6345,spatio_temporal_aggregation,Which station experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2022, which station ranked with the second smallest decrease in 25th percentile PM10 levels from October to December?","Town Hall - Lal Bagh, Darbhanga - BSPCB" 5080,6346,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the state that saw the second most significant fall in median PM2.5 levels when comparing December 2023 to October 2023.,Kerala 5081,6347,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its average PM10 levels between October and December 2018?,Telangana 5082,6350,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2018 to October 2018, which state showed the second least significant drop in 25th percentile PM10 levels?",Jharkhand 5083,6352,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2022, which city ranked with the second largest decrease in average PM2.5 levels from October to December?",Nandesari 5084,6353,spatio_temporal_aggregation,Which city experienced the lowest median PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the city that saw the least significant fall in median PM10 levels when comparing December 2020 to October 2020.,Durgapur 5085,6354,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its median PM2.5 levels between October and December 2022?,Bettiah 5086,6355,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2024, which station had the second smallest decrease in average PM2.5 levels?","Central Academy for SFS, Byrnihat - PCBA" 5087,6357,spatio_temporal_aggregation,Which state experienced the highest median PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2019 to October 2019, which state showed the most significant drop in median PM2.5 levels?",Meghalaya 5088,6358,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city exhibited the second largest decrease in its average PM2.5 levels between October and December of 2021?,Nandesari 5089,6359,spatio_temporal_aggregation,Which city experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2022, which city ranked with the third largest decrease in 75th percentile PM2.5 levels from October to December?",Koppal 5090,6360,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the state that saw the second most significant fall in median PM10 levels when comparing December 2021 to October 2021.,Meghalaya 5091,6361,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its 75th percentile PM10 levels between October and December 2024?,Byrnihat 5092,6362,spatio_temporal_aggregation,Which city experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2023, which city had the largest decrease in 75th percentile PM2.5 levels?",Akola 5093,6363,spatio_temporal_aggregation,Which station experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2019, which station will rank third for the smallest reduction in 25th percentile PM10 levels from October to December?","Jahangirpuri, Delhi - DPCC" 5094,6364,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2021 to October 2021, which state showed the second least significant drop in 25th percentile PM10 levels?",Delhi 5095,6365,spatio_temporal_aggregation,Which station experienced the highest median PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station exhibited the largest decrease in its median PM2.5 levels between October and December of 2019?,"F-Block, Sirsa - HSPCB" 5096,6366,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2022, which city ranked with the largest decrease in 25th percentile PM10 levels from October to December?",Jorapokhar 5097,6367,spatio_temporal_aggregation,Which station experienced the highest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station that saw the most significant fall in median PM2.5 levels when comparing December 2023 to October 2023.,"IIPHG Lekawada, Gandhinagar - IITM" 5098,6368,spatio_temporal_aggregation,Which city experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city experienced the second most significant drop in its 75th percentile PM2.5 levels between October and December 2022?,Hapur 5099,6369,spatio_temporal_aggregation,Which station experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2022, which station had the third smallest decrease in 75th percentile PM10 levels?","Police Line, Saharsa - BSPCB" 5100,6370,spatio_temporal_aggregation,Which state experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2019, which state will rank with the third largest reduction in 25th percentile PM2.5 levels from October to December?",Kerala 5101,6371,spatio_temporal_aggregation,Which city experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","Comparing December 2020 to October 2020, which city showed the second most significant drop in 25th percentile PM10 levels?",Ambala 5102,6372,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which state exhibited the second smallest decrease in its 25th percentile PM2.5 levels between October and December of 2024?,West Bengal 5103,6373,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2019, which city ranked with the largest decrease in 25th percentile PM2.5 levels from October to December?",Sirsa 5104,6374,spatio_temporal_aggregation,Which state experienced the highest median PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that saw the most significant fall in median PM2.5 levels when comparing December 2020 to October 2020.,Punjab 5105,6375,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which state experienced the third least significant drop in its average PM10 levels between October and December 2018?,West Bengal 5106,6377,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2024, which state will rank with the second largest reduction in median PM2.5 levels from October to December?",Himachal Pradesh 5107,6378,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2022 to October 2022, which station showed the second least significant drop in median PM2.5 levels?","Mariam Nagar, Purnia - BSPCB" 5108,6380,spatio_temporal_aggregation,Which station experienced the highest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2024, which station ranked with the largest decrease in average PM2.5 levels from October to December?","CRRI Mathura Road, Delhi - IMD" 5109,6381,spatio_temporal_aggregation,Which state experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the state that saw the second least significant fall in 75th percentile PM2.5 levels when comparing December 2021 to October 2021.,Bihar 5110,6382,spatio_temporal_aggregation,Which state experienced the lowest average PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which state experienced the least significant drop in its average PM2.5 levels between October and December 2021?,Delhi 5111,6383,spatio_temporal_aggregation,Which state experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2021, which state had the third smallest decrease in 75th percentile PM10 levels?",West Bengal 5112,6384,spatio_temporal_aggregation,Which station experienced the highest 25th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2023, which station will rank with the largest reduction in 25th percentile PM10 levels from October to December?","Bandhavgar Colony, Satna - Birla Cement" 5113,6385,spatio_temporal_aggregation,Which city experienced the highest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2024 to October 2024, which city showed the most significant drop in median PM2.5 levels?",Hapur 5114,6386,spatio_temporal_aggregation,Which city experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city exhibited the third largest decrease in its 75th percentile PM2.5 levels between October and December of 2018?,Ahmedabad 5115,6389,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city experienced the most significant drop in its 25th percentile PM2.5 levels between October and December 2024?,Hapur 5116,6390,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","For the period October to December 2022, which station had the third largest decrease in 25th percentile PM2.5 levels?","Gulzarpet, Anantapur - APPCB" 5117,6391,spatio_temporal_aggregation,Which city experienced the lowest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2021, which city will rank with the smallest reduction in median PM10 levels from October to December?",Muzaffarpur 5118,6392,spatio_temporal_aggregation,Which station experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2019 to October 2019, which station showed the most significant drop in 75th percentile PM10 levels?","Ardhali Bazar, Varanasi - UPPCB" 5119,6394,spatio_temporal_aggregation,Which city experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2023, which city ranked with the second smallest decrease in median PM2.5 levels from October to December?",Saharsa 5120,6397,spatio_temporal_aggregation,Which state experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2019, which state had the largest decrease in 75th percentile PM2.5 levels?",Meghalaya 5121,6398,spatio_temporal_aggregation,Which state experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2021, which state will rank with the third largest reduction in 75th percentile PM10 levels from October to December?",Chhattisgarh 5122,6399,spatio_temporal_aggregation,Which city experienced the 3rd lowest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2020 to October 2020, which city showed the third least significant drop in 75th percentile PM10 levels?",Patna 5123,6401,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2020, which station ranked with the third largest decrease in 25th percentile PM10 levels from October to December?","Sector-51, Gurugram - HSPCB" 5124,6402,spatio_temporal_aggregation,Which city experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the city that saw the second most significant fall in 25th percentile PM2.5 levels when comparing December 2019 to October 2019.,Hapur 5125,6403,spatio_temporal_aggregation,Which station experienced the 2nd highest 75th percentile of PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station experienced the second most significant drop in its 75th percentile PM10 levels between October and December 2024?,"Wazirpur, Delhi - DPCC" 5126,6404,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2020, which city had the second smallest decrease in average PM2.5 levels?",Kolkata 5127,6405,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2018, which city will rank second for the smallest reduction in 75th percentile PM10 levels from October to December?",Kolkata 5128,6406,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2023 to October 2023, which city showed the second least significant drop in average PM2.5 levels?",Saharsa 5129,6407,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which station exhibited the second smallest decrease in its average PM10 levels between October and December of 2022?,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 5130,6408,spatio_temporal_aggregation,Which station experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2023, which station ranked with the largest decrease in 75th percentile PM2.5 levels from October to December?","Sector-19A Nerul, Navi Mumbai - IITM" 5131,6409,spatio_temporal_aggregation,Which station experienced the 3rd lowest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the station that saw the third least significant fall in average PM10 levels when comparing December 2018 to October 2018.,"Burari Crossing, Delhi - IMD" 5132,6410,spatio_temporal_aggregation,Which city experienced the 3rd lowest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its average PM10 levels between October and December 2018?,Howrah 5133,6411,spatio_temporal_aggregation,Which city experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2024, which city had the second smallest decrease in median PM2.5 levels?",Saharsa 5134,6412,spatio_temporal_aggregation,Which station experienced the highest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2021, which station will rank with the largest reduction in average PM10 levels from October to December?","Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 5135,6413,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2018 to October 2018, which city showed the third most significant drop in 25th percentile PM2.5 levels?",Ludhiana 5136,6414,spatio_temporal_aggregation,Which station experienced the 2nd highest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which station exhibited the second largest decrease in its average PM10 levels between October and December of 2024?,"Sanjay Nagar, Ghaziabad - UPPCB" 5137,6416,spatio_temporal_aggregation,Which city experienced the 2nd lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the city that saw the second least significant fall in 25th percentile PM10 levels when comparing December 2018 to October 2018.,Kolkata 5138,6417,spatio_temporal_aggregation,Which station experienced the 3rd highest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which station experienced the third most significant drop in its 75th percentile PM10 levels between October and December 2020?,"Anand Vihar, Hapur - UPPCB" 5139,6419,spatio_temporal_aggregation,Which state experienced the 2nd highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2023, which state will rank with the second largest reduction in 75th percentile PM2.5 levels from October to December?",Mizoram 5140,6420,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","Comparing December 2020 to October 2020, which state showed the third most significant drop in average PM10 levels?",Chandigarh 5141,6422,spatio_temporal_aggregation,Which station experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2024, which station ranked with the third smallest decrease in median PM2.5 levels from October to December?","Trivenidevi Bhalotia College, Asansol - WBPCB" 5142,6423,spatio_temporal_aggregation,Which state experienced the lowest average PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in average PM10 levels when comparing December 2023 to October 2023.,Bihar 5143,6424,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which station experienced the second least significant drop in its median PM10 levels between October and December 2019?,"Nathu Colony, Ballabgarh - HSPCB" 5144,6425,spatio_temporal_aggregation,Which city experienced the highest average PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For the period October to December 2020, which city had the largest decrease in average PM2.5 levels?",Charkhi Dadri 5145,6426,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2021, which city will rank third for the smallest reduction in median PM10 levels from October to December?",Manesar 5146,6427,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2018 to October 2018, which city showed the third least significant drop in 25th percentile PM2.5 levels?",Muzaffarpur 5147,6428,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which state exhibited the third largest decrease in its median PM2.5 levels between October and December of 2023?,Telangana 5148,6430,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the state that saw the least significant fall in 75th percentile PM10 levels when comparing December 2018 to October 2018.,Odisha 5149,6431,spatio_temporal_aggregation,Which station experienced the lowest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which station experienced the least significant drop in its median PM10 levels between October and December 2023?,"Samanpura, Patna - BSPCB" 5150,6432,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2019, which city had the second smallest decrease in 75th percentile PM2.5 levels?",Ghaziabad 5151,6433,spatio_temporal_aggregation,Which state experienced the highest average PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2018, which state will rank with the largest reduction in average PM2.5 levels from October to December?",Gujarat 5152,6434,spatio_temporal_aggregation,Which state experienced the lowest average PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2020 to October 2020, which state showed the least significant drop in average PM10 levels?",Assam 5153,6435,spatio_temporal_aggregation,Which station experienced the highest 75th percentile of PM10 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station exhibited the largest decrease in its 75th percentile PM10 levels between October and December of 2020?,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 5154,6436,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2022, which city ranked with the third smallest decrease in median PM10 levels from October to December?",Arrah 5155,6437,spatio_temporal_aggregation,Which city experienced the lowest median PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the city that saw the least significant fall in median PM2.5 levels when comparing December 2018 to October 2018.,Muzaffarpur 5156,6438,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its 25th percentile PM10 levels between October and December 2018?,Howrah 5157,6440,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2023, which station will rank with the second largest reduction in 25th percentile PM10 levels from October to December?","Prabhat Colony, Jalgaon - MPCB" 5158,6441,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2019 to October 2019, which state showed the third least significant drop in average PM2.5 levels?",Assam 5159,6442,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which state exhibited the third largest decrease in its median PM2.5 levels between October and December of 2021?,Meghalaya 5160,6443,spatio_temporal_aggregation,Which city experienced the 2nd highest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2019, which city ranked with the second largest decrease in average PM10 levels from October to December?",Sirsa 5161,6444,spatio_temporal_aggregation,Which city experienced the 2nd highest 25th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Identify the city that saw the second most significant fall in 25th percentile PM10 levels when comparing December 2018 to October 2018.,Dewas 5162,6445,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which city experienced the third least significant drop in its 25th percentile PM10 levels between October and December 2023?,Araria 5163,6446,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2021, which station had the second smallest decrease in median PM2.5 levels?","Nehru Nagar, Delhi - DPCC" 5164,6448,spatio_temporal_aggregation,Which station experienced the 2nd lowest median PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2023 to October 2023, which station showed the second least significant drop in median PM10 levels?","DRCC Anandpur, Begusarai - BSPCB" 5165,6449,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which state exhibited the third smallest decrease in its median PM2.5 levels between October and December of 2021?,Haryana 5166,6452,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its median PM2.5 levels between October and December 2021?,Mizoram 5167,6453,spatio_temporal_aggregation,Which state experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2020, which state had the smallest decrease in 75th percentile PM2.5 levels?",Bihar 5168,6454,spatio_temporal_aggregation,Which state experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2022, which state will rank with the third largest reduction in 75th percentile PM2.5 levels from October to December?",Arunachal Pradesh 5169,6455,spatio_temporal_aggregation,Which state experienced the lowest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2023 to October 2023, which state showed the least significant drop in average PM2.5 levels?",Delhi 5170,6456,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Which city exhibited the second smallest decrease in its 75th percentile PM2.5 levels between October and December of 2023?,Saharsa 5171,6457,spatio_temporal_aggregation,Which state experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2022, which state ranked with the smallest decrease in 25th percentile PM10 levels from October to December?",Bihar 5172,6459,spatio_temporal_aggregation,Which city experienced the 2nd highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which city experienced the second most significant drop in its median PM10 levels between October and December 2019?,Sonipat 5173,6460,spatio_temporal_aggregation,Which city experienced the 2nd lowest average PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","For the period October to December 2024, which city had the second smallest decrease in average PM2.5 levels?",Saharsa 5174,6461,spatio_temporal_aggregation,Which state experienced the 3rd highest 75th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2023, which state will rank with the third largest reduction in 75th percentile PM2.5 levels from October to December?",Karnataka 5175,6462,spatio_temporal_aggregation,Which city experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","Comparing December 2023 to October 2023, which city showed the least significant drop in 25th percentile PM10 levels?",Begusarai 5176,6463,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which state exhibited the third largest decrease in its average PM10 levels between October and December of 2024?,Uttar Pradesh 5177,6465,spatio_temporal_aggregation,Which station experienced the 2nd lowest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the station that saw the second least significant fall in average PM10 levels when comparing December 2021 to October 2021.,"Vivek Vihar, Delhi - DPCC" 5178,6466,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which state experienced the third least significant drop in its median PM2.5 levels between October and December 2024?,Bihar 5179,6467,spatio_temporal_aggregation,Which station experienced the lowest median PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","For the period October to December 2022, which station had the smallest decrease in median PM10 levels?","Samanpura, Patna - BSPCB" 5180,6468,spatio_temporal_aggregation,Which station experienced the 2nd highest 25th percentile of PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2023, which station will rank with the second largest reduction in 25th percentile PM2.5 levels from October to December?","Arya Nagar, Bahadurgarh - HSPCB" 5181,6469,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2018 to October 2018, which state showed the third least significant drop in median PM2.5 levels?",Uttar Pradesh 5182,6470,spatio_temporal_aggregation,Which city experienced the 3rd highest 25th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city exhibited the third largest decrease in its 25th percentile PM10 levels between October and December of 2021?,Chamarajanagar 5183,6471,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","In 2018, which state ranked with the third largest decrease in average PM10 levels from October to December?",Madhya Pradesh 5184,6472,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Identify the state that saw the third least significant fall in median PM2.5 levels when comparing December 2019 to October 2019.,Assam 5185,6473,spatio_temporal_aggregation,Which state experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state experienced the most significant drop in its 25th percentile PM2.5 levels between October and December 2019?,Meghalaya 5186,6474,spatio_temporal_aggregation,Which station experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2020, which station had the third smallest decrease in 25th percentile PM2.5 levels?","IGSC Planetarium Complex, Patna - BSPCB" 5187,6475,spatio_temporal_aggregation,Which city experienced the lowest 75th percentile of PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2022, which city will rank with the smallest reduction in 75th percentile PM2.5 levels from October to December?",Begusarai 5188,6476,spatio_temporal_aggregation,Which state experienced the 2nd highest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","Comparing December 2022 to October 2022, which state showed the second most significant drop in median PM2.5 levels?",Mizoram 5189,6477,spatio_temporal_aggregation,Which station experienced the highest 75th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station exhibited the largest decrease in its 75th percentile PM2.5 levels between October and December of 2021?,"Anthoni Pillai Nagar, Gummidipoondi - TNPCB" 5190,6478,spatio_temporal_aggregation,Which station experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ","In 2018, which station ranked with the smallest decrease in 75th percentile PM10 levels from October to December?","Talcher Coalfields,Talcher - OSPCB" 5191,6479,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the state that saw the second least significant fall in 25th percentile PM2.5 levels when comparing December 2021 to October 2021.,Bihar 5192,6480,spatio_temporal_aggregation,Which state experienced the 3rd highest median PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which state experienced the third most significant drop in its median PM10 levels between October and December 2019?,Chandigarh 5193,6481,spatio_temporal_aggregation,Which station experienced the 3rd highest 25th percentile of PM2.5 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ","For the period October to December 2021, which station had the third largest decrease in 25th percentile PM2.5 levels?","GIDC, Nandesari - Nandesari Ind. Association" 5194,6482,spatio_temporal_aggregation,Which state experienced the 2nd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","In 2020, which state will rank with the second smallest reduction in 25th percentile PM2.5 levels from October to December?",Assam 5195,6483,spatio_temporal_aggregation,Which state experienced the 3rd lowest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","Comparing December 2024 to October 2024, which state showed the third least significant drop in average PM10 levels?",West Bengal 5196,6485,spatio_temporal_aggregation,Which station experienced the 3rd lowest average PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2021, which station ranked with the third smallest decrease in average PM10 levels from October to December?","Sector - 62, Noida - IMD" 5197,6486,spatio_temporal_aggregation,Which state experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Identify the state that saw the third most significant fall in average PM2.5 levels when comparing December 2023 to October 2023.,Jharkhand 5198,6487,spatio_temporal_aggregation,Which station experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which station experienced the least significant drop in its 25th percentile PM10 levels between October and December 2022?,"Samanpura, Patna - BSPCB" 5199,6488,spatio_temporal_aggregation,Which station experienced the 3rd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2018, which station had the third smallest decrease in 75th percentile PM2.5 levels?","Nehru Nagar, Delhi - DPCC" 5200,6489,spatio_temporal_aggregation,Which state experienced the 3rd lowest median PM2.5 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2022, which state will rank with the third smallest reduction in median PM2.5 levels from October to December?",Delhi 5201,6490,spatio_temporal_aggregation,Which state experienced the highest median PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2021 to October 2021, which state showed the most significant drop in median PM10 levels?",Chhattisgarh 5202,6491,spatio_temporal_aggregation,Which station experienced the lowest 75th percentile of PM10 drop compared between October and December in the year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Which station exhibited the smallest decrease in its 75th percentile PM10 levels between October and December of 2022?,"Samanpura, Patna - BSPCB" 5203,6492,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM10 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ","In 2024, which state ranked with the second largest decrease in average PM10 levels from October to December?",Uttarakhand 5204,6493,spatio_temporal_aggregation,Which station experienced the 2nd lowest 75th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ",Identify the station that saw the second least significant fall in 75th percentile PM2.5 levels when comparing December 2024 to October 2024.,"Trivenidevi Bhalotia College, Asansol - WBPCB" 5205,6494,spatio_temporal_aggregation,Which state experienced the 2nd highest average PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""state"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-2].name) true_code() ",Which state experienced the second most significant drop in its average PM2.5 levels between October and December 2018?,Punjab 5206,6495,spatio_temporal_aggregation,Which city experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2024, which city had the third smallest decrease in 25th percentile PM2.5 levels?",Barrackpore 5207,6496,spatio_temporal_aggregation,Which station experienced the 3rd lowest average PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","In 2019, which station will rank third for the smallest reduction in average PM10 levels from October to December?","Nathu Colony, Ballabgarh - HSPCB" 5208,6497,spatio_temporal_aggregation,Which city experienced the 2nd lowest 75th percentile of PM10 drop compared between October and December in the year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[1].name) true_code() ","Comparing December 2021 to October 2021, which city showed the second least significant drop in 75th percentile PM10 levels?",Gaya 5209,6498,spatio_temporal_aggregation,Which station experienced the 3rd lowest 25th percentile of PM2.5 drop compared between October and December in the year 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""station"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ",Which station exhibited the third smallest decrease in its 25th percentile PM2.5 levels between October and December of 2024?,"Botanical Garden, Howrah - WBPCB" 5210,6500,spatio_temporal_aggregation,Which city experienced the lowest 25th percentile of PM10 drop compared between October and December in the year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[0].name) true_code() ",Identify the city that saw the least significant fall in 25th percentile PM10 levels when comparing December 2019 to October 2019.,Talcher 5211,6501,spatio_temporal_aggregation,Which city experienced the 3rd highest average PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-3].name) true_code() ",Which city experienced the third most significant drop in its average PM2.5 levels between October and December 2018?,Alwar 5212,6502,spatio_temporal_aggregation,Which city experienced the 3rd lowest median PM10 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM10') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[2].name) true_code() ","For the period October to December 2018, which city had the third smallest decrease in median PM10 levels?",Howrah 5213,6506,spatio_temporal_aggregation,Which city experienced the highest 25th percentile of PM2.5 drop compared between October and December in the year 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[(data['Timestamp'].dt.month == 10) | (data['Timestamp'].dt.month == 12)] data = data.groupby([""city"", data['Timestamp'].dt.month])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns='Timestamp', values='PM2.5') data['diff'] = data[10] - data[12] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","In 2018, which city ranked with the largest decrease in 25th percentile PM2.5 levels from October to December?",Chandrapur 5214,6508,spatio_temporal_aggregation,What is the median PM10 on Thursday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].median()) true_code() ",What is the median PM10 level on Thursdays in West Bengal?,85.5251111111111 5215,6509,spatio_temporal_aggregation,What is the average PM10 on Friday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 4] print(data[""PM10""].mean()) true_code() ","In Bihar, what is the average PM10 concentration on Fridays?",157.2916033043651 5216,6510,spatio_temporal_aggregation,What is the average PM2.5 on Thursday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM2.5""].mean()) true_code() ",Determine the average PM2.5 level on Thursdays in Maharashtra.,44.246437194873515 5217,6512,spatio_temporal_aggregation,What is the median PM2.5 on Monday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 0] print(data[""PM2.5""].median()) true_code() ","In Maharashtra, what is the median PM2.5 concentration on Mondays?",35.101021381578946 5218,6514,spatio_temporal_aggregation,What is the median PM10 on Friday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 4] print(data[""PM10""].median()) true_code() ",What is the median PM10 value on Fridays in Bihar?,131.94768421052635 5219,6515,spatio_temporal_aggregation,What is the average PM10 on Sunday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM10""].mean()) true_code() ","In Bihar, what is the average PM10 concentration on Sundays?",154.7683224091028 5220,6516,spatio_temporal_aggregation,What is the median PM2.5 on Sunday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM2.5""].median()) true_code() ",Determine the median PM2.5 level on Sundays in West Bengal.,38.76263157894736 5221,6518,spatio_temporal_aggregation,What is the average PM2.5 on Tuesday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM2.5""].mean()) true_code() ","In Madhya Pradesh, what is the average PM2.5 concentration on Tuesdays?",46.014547375036734 5222,6520,spatio_temporal_aggregation,What is the average PM10 on Thursday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].mean()) true_code() ",What is the mean PM10 value on Thursdays in Bihar?,156.75745291271926 5223,6521,spatio_temporal_aggregation,What is the median PM2.5 on Saturday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM2.5""].median()) true_code() ","In Uttar Pradesh, what is the median PM2.5 concentration on Saturdays?",50.40625 5224,6522,spatio_temporal_aggregation,What is the median PM2.5 on Sunday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM2.5""].median()) true_code() ",Determine the median PM2.5 level on Sundays in Madhya Pradesh.,34.9984375 5225,6523,spatio_temporal_aggregation,What is the median PM10 on Tuesday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM10""].median()) true_code() ",What is the median PM10 value on Tuesdays in Bihar?,134.57664160401004 5226,6524,spatio_temporal_aggregation,What is the average PM10 on Friday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 4] print(data[""PM10""].mean()) true_code() ","In Uttar Pradesh, what is the average PM10 concentration on Fridays?",145.9745974546718 5227,6525,spatio_temporal_aggregation,What is the median PM10 on Sunday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM10""].median()) true_code() ",Calculate the median PM10 level on Sundays in Uttar Pradesh.,116.41666666666669 5228,6526,spatio_temporal_aggregation,What is the median PM2.5 on Tuesday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM2.5""].median()) true_code() ",What is the median PM2.5 value on Tuesdays in Madhya Pradesh?,35.74885416666667 5229,6528,spatio_temporal_aggregation,What is the average PM10 on Tuesday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM10""].mean()) true_code() ",Determine the average PM10 level on Tuesdays in Maharashtra.,98.52620917088724 5230,6531,spatio_temporal_aggregation,What is the average PM2.5 on Friday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 4] print(data[""PM2.5""].mean()) true_code() ",Calculate the average PM2.5 level on Fridays in Uttar Pradesh.,70.86011153610465 5231,6533,spatio_temporal_aggregation,What is the median PM10 on Saturday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM10""].median()) true_code() ","In Uttar Pradesh, what is the median PM10 concentration on Saturdays?",116.84375 5232,6534,spatio_temporal_aggregation,What is the median PM10 on Saturday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM10""].median()) true_code() ",Determine the median PM10 level on Saturdays in Madhya Pradesh.,95.86791666666666 5233,6536,spatio_temporal_aggregation,What is the median PM2.5 on Wednesday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 2] print(data[""PM2.5""].median()) true_code() ","In Uttar Pradesh, what is the median PM2.5 concentration on Wednesdays?",50.53770833333333 5234,6538,spatio_temporal_aggregation,What is the average PM10 on Thursday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].mean()) true_code() ",What is the mean PM10 value on Thursdays in Uttar Pradesh?,147.5202513175197 5235,6539,spatio_temporal_aggregation,What is the median PM10 on Sunday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM10""].median()) true_code() ","In West Bengal, what is the median PM10 concentration on Sundays?",80.9653012048193 5236,6541,spatio_temporal_aggregation,What is the average PM10 on Thursday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].mean()) true_code() ",What is the mean PM10 value on Thursdays in West Bengal?,108.91824756801634 5237,6543,spatio_temporal_aggregation,What is the median PM2.5 on Thursday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM2.5""].median()) true_code() ",Calculate the median PM2.5 level on Thursdays in Maharashtra.,36.515 5238,6544,spatio_temporal_aggregation,What is the median PM10 on Saturday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM10""].median()) true_code() ",What is the median PM10 value on Saturdays in Bihar?,132.08647058823527 5239,6545,spatio_temporal_aggregation,What is the median PM10 on Thursday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].median()) true_code() ","In Madhya Pradesh, what is the median PM10 concentration on Thursdays?",97.2 5240,6546,spatio_temporal_aggregation,What is the average PM10 on Wednesday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 2] print(data[""PM10""].mean()) true_code() ",Determine the average PM10 level on Wednesdays in Bihar.,157.07935162667667 5241,6548,spatio_temporal_aggregation,What is the median PM10 on Sunday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM10""].median()) true_code() ","In Maharashtra, what is the median PM10 concentration on Sundays?",82.6295283018868 5242,6549,spatio_temporal_aggregation,What is the average PM2.5 on Monday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 0] print(data[""PM2.5""].mean()) true_code() ",Calculate the average PM2.5 level on Mondays in Maharashtra.,42.839415180559136 5243,6550,spatio_temporal_aggregation,What is the average PM10 on Saturday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM10""].mean()) true_code() ",What is the mean PM10 value on Saturdays in Maharashtra?,97.32283057595629 5244,6551,spatio_temporal_aggregation,What is the median PM10 on Thursday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 3] print(data[""PM10""].median()) true_code() ","In Uttar Pradesh, what is the median PM10 concentration on Thursdays?",118.12416666666674 5245,6552,spatio_temporal_aggregation,What is the average PM10 on Wednesday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 2] print(data[""PM10""].mean()) true_code() ",Determine the average PM10 level on Wednesdays in West Bengal.,110.14290958149363 5246,6553,spatio_temporal_aggregation,What is the median PM2.5 on Monday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 0] print(data[""PM2.5""].median()) true_code() ",What is the median PM2.5 value on Mondays in Madhya Pradesh?,35.708333333333336 5247,6554,spatio_temporal_aggregation,What is the average PM2.5 on Monday in Madhya Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Madhya Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 0] print(data[""PM2.5""].mean()) true_code() ","In Madhya Pradesh, what is the average PM2.5 concentration on Mondays?",45.944136375356315 5248,6555,spatio_temporal_aggregation,What is the average PM10 on Tuesday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM10""].mean()) true_code() ",Calculate the average PM10 level on Tuesdays in Bihar.,158.87422561777768 5249,6556,spatio_temporal_aggregation,What is the median PM2.5 on Wednesday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 2] print(data[""PM2.5""].median()) true_code() ",What is the median PM2.5 value on Wednesdays in Maharashtra?,36.07467391304348 5250,6557,spatio_temporal_aggregation,What is the median PM2.5 on Tuesday in Uttar Pradesh ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Uttar Pradesh""] data = data[main_data[""Timestamp""].dt.dayofweek == 1] print(data[""PM2.5""].median()) true_code() ","In Uttar Pradesh, what is the median PM2.5 concentration on Tuesdays?",50.625 5251,6559,spatio_temporal_aggregation,What is the average PM10 on Friday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 4] print(data[""PM10""].mean()) true_code() ",What is the mean PM10 value on Fridays in Maharashtra?,97.82913222917868 5252,6562,spatio_temporal_aggregation,What is the median PM2.5 on Sunday in Bihar ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Bihar""] data = data[main_data[""Timestamp""].dt.dayofweek == 6] print(data[""PM2.5""].median()) true_code() ",What is the median PM2.5 value on Sundays in Bihar?,57.21200000000001 5253,6563,spatio_temporal_aggregation,What is the median PM2.5 on Wednesday in West Bengal ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""West Bengal""] data = data[main_data[""Timestamp""].dt.dayofweek == 2] print(data[""PM2.5""].median()) true_code() ","In West Bengal, what is the median PM2.5 concentration on Wednesdays?",40.711052631578944 5254,6564,spatio_temporal_aggregation,What is the median PM2.5 on Saturday in Maharashtra ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""state""] == ""Maharashtra""] data = data[main_data[""Timestamp""].dt.dayofweek == 5] print(data[""PM2.5""].median()) true_code() ",Determine the median PM2.5 level on Saturdays in Maharashtra.,35.801530612244896 5255,6565,spatio_temporal_aggregation,Which state had the median PM10 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state observed the most significant increase in median PM10 levels when comparing October 2019 to October 2020?,Delhi 5256,6566,spatio_temporal_aggregation,Which city had the 25th percentile of PM10 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing February 2019 with February 2020, which city showed the largest rise in its 25th percentile PM10 level?",Panipat 5257,6567,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For May 2020 relative to May 2019, which station recorded the highest increase in the 25th percentile of PM10 levels?","Manali Village, Chennai - TNPCB" 5258,6570,spatio_temporal_aggregation,Which state had the average PM2.5 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between October 2019 and October 2020, which state saw the largest upsurge in average PM2.5 levels?",Andhra Pradesh 5259,6571,spatio_temporal_aggregation,Which state had the median PM10 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state had the most pronounced increase in median PM10 levels when comparing November 2019 to November 2020?,Delhi 5260,6572,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For May 2020 compared to May 2019, which city registered the highest increase in the 75th percentile of PM2.5 levels?",Kurukshetra 5261,6573,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to June 2020 from June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 6] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that showed the highest rise in its 75th percentile PM2.5 level from June 2019 to June 2020.,Odisha 5262,6574,spatio_temporal_aggregation,Which station had the average PM2.5 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing April 2019 with April 2020, which station experienced the largest increase in average PM2.5 levels?","Pusa, Delhi - IMD" 5263,6578,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For April 2020 relative to April 2019, which station had the most substantial increase in the 75th percentile of PM10 levels?","Manali Village, Chennai - TNPCB" 5264,6580,spatio_temporal_aggregation,Which station had the median PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing January 2019 with January 2020, which station showed the largest increase in median PM2.5 levels?","Nathu Colony, Ballabgarh - HSPCB" 5265,6581,spatio_temporal_aggregation,Which state had the average PM2.5 level increased most compared to June 2020 from June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 6] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that saw the most significant growth in average PM2.5 levels from June 2019 to June 2020.,Odisha 5266,6584,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between April 2019 and April 2020, which state saw the largest upsurge in its 75th percentile PM2.5 level?",Assam 5267,6585,spatio_temporal_aggregation,Which station had the 75th percentile of PM2.5 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station where the 75th percentile of PM2.5 levels rose most significantly from April 2019 to April 2020.,"Pusa, Delhi - IMD" 5268,6586,spatio_temporal_aggregation,Which state had the 75th percentile of PM10 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing January 2019 with January 2020, which state experienced the largest increase in its 75th percentile PM10 level?",Kerala 5269,6587,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to June 2020 from June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 6] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For June 2020 relative to June 2019, which city recorded the highest increase in average PM10 levels?",Kalaburagi 5270,6588,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station had the most substantial increase in the 75th percentile of PM10 levels between November 2019 and November 2020?,"Murthal, Sonipat - HSPCB" 5271,6589,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that showed the highest rise in its 75th percentile PM2.5 level from February 2019 to February 2020.,Assam 5272,6590,spatio_temporal_aggregation,Which station had the 25th percentile of PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing November 2019 with November 2020, which station experienced the largest increase in its 25th percentile PM2.5 level?","Huda Sector, Fatehabad - HSPCB" 5273,6591,spatio_temporal_aggregation,Which city had the median PM10 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city recorded the most significant growth in median PM10 levels between September 2019 and September 2020?,Hapur 5274,6593,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city where average PM10 levels increased the most from April 2019 to April 2020.,Chennai 5275,6596,spatio_temporal_aggregation,Which city had the 75th percentile of PM10 level increased most compared to June 2020 from June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 6] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing June 2019 with June 2020, which city showed the largest increase in its 75th percentile PM10 level?",Kalaburagi 5276,6597,spatio_temporal_aggregation,Which city had the 25th percentile of PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant growth in its 25th percentile PM2.5 level from November 2019 to November 2020.,Fatehabad 5277,6598,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For September 2020 compared to September 2019, which station registered the highest increase in median PM10 levels?","Anand Vihar, Hapur - UPPCB" 5278,6599,spatio_temporal_aggregation,Which city had the 75th percentile of PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city had the most pronounced increase in its 75th percentile PM10 level when comparing April 2019 to April 2020?,Chennai 5279,6600,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between January 2019 and January 2020, which city saw the largest upsurge in its 75th percentile PM2.5 level?",Talcher 5280,6601,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station where the median PM10 level rose most significantly from April 2019 to April 2020.,"Manali Village, Chennai - TNPCB" 5281,6602,spatio_temporal_aggregation,Which city had the median PM10 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing December 2019 with December 2020, which city experienced the largest increase in median PM10 levels?",Varanasi 5282,6603,spatio_temporal_aggregation,Which station had the average PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For November 2020 relative to November 2019, which station recorded the highest increase in average PM2.5 levels?","Huda Sector, Fatehabad - HSPCB" 5283,6607,spatio_temporal_aggregation,Which city had the 25th percentile of PM2.5 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city recorded the most significant growth in its 25th percentile PM2.5 level between December 2019 and December 2020?,Agra 5284,6608,spatio_temporal_aggregation,Which city had the median PM10 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between January 2019 and January 2020, which city saw the largest upswing in median PM10 levels?",Panipat 5285,6609,spatio_temporal_aggregation,Which state had the median PM2.5 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state where median PM2.5 levels increased the most from July 2019 to July 2020.,Kerala 5286,6610,spatio_temporal_aggregation,Which state had the average PM10 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For November 2020 relative to November 2019, which state had the most substantial increase in average PM10 levels?",Uttar Pradesh 5287,6611,spatio_temporal_aggregation,Which state had the median PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state experienced the highest rise in median PM2.5 levels between January 2019 and January 2020?,Punjab 5288,6612,spatio_temporal_aggregation,Which city had the 25th percentile of PM10 level increased most compared to March 2020 from March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 3] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing March 2019 with March 2020, which city showed the largest increase in its 25th percentile PM10 level?",Panipat 5289,6613,spatio_temporal_aggregation,Which city had the 25th percentile of PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant growth in its 25th percentile PM2.5 level from January 2019 to January 2020.,Talcher 5290,6614,spatio_temporal_aggregation,Which city had the 25th percentile of PM10 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For December 2020 compared to December 2019, which city registered the highest increase in its 25th percentile PM10 level?",Varanasi 5291,6616,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between February 2019 and February 2020, which station saw the largest upsurge in its 25th percentile PM10 level?","Sector-18, Panipat - HSPCB" 5292,6617,spatio_temporal_aggregation,Which city had the average PM2.5 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city where average PM2.5 levels rose most significantly from February 2019 to February 2020.,Guwahati 5293,6620,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city had the most substantial increase in its 75th percentile PM2.5 level between December 2019 and December 2020?,Agra 5294,6621,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station that showed the highest rise in its 75th percentile PM10 level from February 2019 to February 2020.,"Sector-18, Panipat - HSPCB" 5295,6622,spatio_temporal_aggregation,Which state had the average PM2.5 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing July 2019 with July 2020, which state experienced the largest increase in average PM2.5 levels?",Punjab 5296,6623,spatio_temporal_aggregation,Which state had the 75th percentile of PM10 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state recorded the most significant growth in its 75th percentile PM10 level between October 2019 and October 2020?,Telangana 5297,6624,spatio_temporal_aggregation,Which state had the 25th percentile of PM10 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between July 2019 and July 2020, which state saw the largest upswing in its 25th percentile PM10 level?",Jharkhand 5298,6625,spatio_temporal_aggregation,Which state had the 25th percentile of PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state where the 25th percentile of PM10 levels increased the most from April 2019 to April 2020.,Jharkhand 5299,6626,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For August 2020 relative to August 2019, which state had the most substantial increase in its 75th percentile PM2.5 level?",Chandigarh 5300,6628,spatio_temporal_aggregation,Which station had the average PM2.5 level increased most compared to March 2020 from March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 3] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing March 2019 with March 2020, which station showed the largest increase in average PM2.5 levels?","BWSSB Kadabesanahalli, Bengaluru - CPCB" 5301,6630,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For April 2020 compared to April 2019, which station registered the highest increase in its 25th percentile PM10 level?","Manali Village, Chennai - TNPCB" 5302,6631,spatio_temporal_aggregation,Which state had the average PM10 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state had the most pronounced increase in average PM10 levels when comparing August 2019 to August 2020?,Kerala 5303,6632,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between May 2019 and May 2020, which station saw the largest upsurge in its 75th percentile PM10 level?","Manali Village, Chennai - TNPCB" 5304,6633,spatio_temporal_aggregation,Which state had the 75th percentile of PM10 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state where the 75th percentile of PM10 levels rose most significantly from September 2019 to September 2020.,Uttar Pradesh 5305,6634,spatio_temporal_aggregation,Which state had the average PM10 level increased most compared to April 2020 from April 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 4] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing April 2019 with April 2020, which state experienced the largest increase in average PM10 levels?",Tamil Nadu 5306,6635,spatio_temporal_aggregation,Which state had the average PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For November 2020 relative to November 2019, which state recorded the highest increase in average PM2.5 levels?",Kerala 5307,6636,spatio_temporal_aggregation,Which station had the average PM10 level increased most compared to March 2020 from March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 3] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station had the most substantial increase in average PM10 levels between March 2019 and March 2020?,"Sector-18, Panipat - HSPCB" 5308,6637,spatio_temporal_aggregation,Which state had the 25th percentile of PM10 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that showed the highest rise in its 25th percentile PM10 level from September 2019 to September 2020.,Odisha 5309,6638,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing January 2019 with January 2020, which city experienced the largest increase in average PM10 levels?",Panipat 5310,6639,spatio_temporal_aggregation,Which state had the median PM10 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state recorded the most significant growth in median PM10 levels between February 2019 and February 2020?,Assam 5311,6640,spatio_temporal_aggregation,Which state had the average PM10 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between May 2019 and May 2020, which state saw the largest upswing in average PM10 levels?",Jharkhand 5312,6642,spatio_temporal_aggregation,Which station had the 75th percentile of PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For November 2020 relative to November 2019, which station had the most substantial increase in its 75th percentile PM2.5 level?","Huda Sector, Fatehabad - HSPCB" 5313,6643,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station experienced the highest rise in its 25th percentile PM10 level between September 2019 and September 2020?,"Sector-1, Noida - UPPCB" 5314,6644,spatio_temporal_aggregation,Which state had the 25th percentile of PM10 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing August 2019 with August 2020, which state showed the largest increase in its 25th percentile PM10 level?",Odisha 5315,6645,spatio_temporal_aggregation,Which state had the 75th percentile of PM2.5 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that saw the most significant growth in its 75th percentile PM2.5 level from November 2019 to November 2020.,Haryana 5316,6646,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For November 2020 compared to November 2019, which city registered the highest increase in average PM10 levels?",Thane 5317,6647,spatio_temporal_aggregation,Which station had the average PM10 level increased most compared to June 2020 from June 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 6] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station had the most pronounced increase in average PM10 levels when comparing June 2019 to June 2020?,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 5318,6648,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to February 2020 from February 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 2] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between February 2019 and February 2020, which city saw the largest upsurge in average PM10 levels?",Panipat 5319,6649,spatio_temporal_aggregation,Which station had the average PM10 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station where average PM10 levels rose most significantly from January 2019 to January 2020.,"Sector-18, Panipat - HSPCB" 5320,6650,spatio_temporal_aggregation,Which state had the 75th percentile of PM10 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing July 2019 with July 2020, which state experienced the largest increase in its 75th percentile PM10 level?",Jharkhand 5321,6652,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station had the most substantial increase in median PM10 levels between May 2019 and May 2020?,"Manali Village, Chennai - TNPCB" 5322,6653,spatio_temporal_aggregation,Which state had the 75th percentile of PM10 level increased most compared to March 2020 from March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 3] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state that showed the highest rise in its 75th percentile PM10 level from March 2019 to March 2020.,Tamil Nadu 5323,6654,spatio_temporal_aggregation,Which station had the median PM2.5 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing August 2019 with August 2020, which station experienced the largest increase in median PM2.5 levels?","Nathu Colony, Ballabgarh - HSPCB" 5324,6655,spatio_temporal_aggregation,Which station had the 25th percentile of PM2.5 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station recorded the most significant growth in its 25th percentile PM2.5 level between May 2019 and May 2020?,"Bandra, Mumbai - MPCB" 5325,6656,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between May 2019 and May 2020, which city saw the largest upswing in average PM10 levels?",Chennai 5326,6657,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station where the 75th percentile of PM10 levels increased the most from October 2019 to October 2020.,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 5327,6658,spatio_temporal_aggregation,Which city had the 75th percentile of PM10 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For July 2020 relative to July 2019, which city had the most substantial increase in its 75th percentile PM10 level?",Mandi Gobindgarh 5328,6659,spatio_temporal_aggregation,Which state had the 25th percentile of PM2.5 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which state experienced the highest rise in its 25th percentile PM2.5 level between August 2019 and August 2020?,Chandigarh 5329,6660,spatio_temporal_aggregation,Which city had the median PM2.5 level increased most compared to May 2020 from May 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 5] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing May 2019 with May 2020, which city showed the largest increase in median PM2.5 levels?",Ratlam 5330,6663,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which city had the most pronounced increase in its 75th percentile PM2.5 level when comparing August 2019 to August 2020?,Ballabgarh 5331,6664,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between December 2019 and December 2020, which city saw the largest upsurge in average PM10 levels?",Varanasi 5332,6665,spatio_temporal_aggregation,Which state had the 25th percentile of PM2.5 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].quantile(0.25).reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the state where the 25th percentile of PM2.5 levels rose most significantly from October 2019 to October 2020.,Uttar Pradesh 5333,6666,spatio_temporal_aggregation,Which city had the average PM10 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM10""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing July 2019 with July 2020, which city experienced the largest increase in average PM10 levels?",Mandi Gobindgarh 5334,6667,spatio_temporal_aggregation,Which station had the 75th percentile of PM10 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For January 2020 relative to January 2019, which station recorded the highest increase in its 75th percentile PM10 level?","Sector-18, Panipat - HSPCB" 5335,6668,spatio_temporal_aggregation,Which station had the 75th percentile of PM2.5 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station had the most substantial increase in its 75th percentile PM2.5 level between September 2019 and September 2020?,"Lalbagh, Lucknow - CPCB" 5336,6669,spatio_temporal_aggregation,Which station had the median PM10 level increased most compared to November 2020 from November 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 11] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station that showed the highest rise in median PM10 levels from November 2019 to November 2020.,"ITO, Delhi - CPCB" 5337,6670,spatio_temporal_aggregation,Which state had the median PM2.5 level increased most compared to August 2020 from August 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 8] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""state"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""state"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing August 2019 with August 2020, which state experienced the largest increase in median PM2.5 levels?",Chandigarh 5338,6671,spatio_temporal_aggregation,Which station had the 75th percentile of PM2.5 level increased most compared to March 2020 from March 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 3] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station recorded the most significant growth in its 75th percentile PM2.5 level between March 2019 and March 2020?,"Bandhavgar Colony, Satna - Birla Cement" 5339,6673,spatio_temporal_aggregation,Which station had the average PM2.5 level increased most compared to December 2020 from December 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 12] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the station where average PM2.5 levels increased the most from December 2019 to December 2020.,"Sanjay Palace, Agra - UPPCB" 5340,6674,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","For October 2020 relative to October 2019, which city had the most substantial increase in its 75th percentile PM2.5 level?",Fatehabad 5341,6675,spatio_temporal_aggregation,Which station had the median PM2.5 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Which station experienced the highest rise in median PM2.5 levels between October 2019 and October 2020?,"Huda Sector, Fatehabad - HSPCB" 5342,6676,spatio_temporal_aggregation,Which station had the 25th percentile of PM10 level increased most compared to October 2020 from October 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 10] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM10""].quantile(0.25).reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM10"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing October 2019 with October 2020, which station showed the largest increase in its 25th percentile PM10 level?","Bawana, Delhi - DPCC" 5343,6677,spatio_temporal_aggregation,Which city had the average PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city that saw the most significant growth in average PM2.5 levels from January 2019 to January 2020.,Talcher 5344,6680,spatio_temporal_aggregation,Which station had the average PM2.5 level increased most compared to January 2020 from January 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 1] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""station"", ""year""])[""PM2.5""].mean().reset_index() data = data.pivot(index=""station"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Between January 2019 and January 2020, which station saw the largest upsurge in average PM2.5 levels?","Talcher Coalfields,Talcher - OSPCB" 5345,6681,spatio_temporal_aggregation,Which city had the median PM2.5 level increased most compared to July 2020 from July 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 7] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].median().reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ",Identify the city where median PM2.5 levels rose most significantly from July 2019 to July 2020.,Mandi Gobindgarh 5346,6682,spatio_temporal_aggregation,Which city had the 75th percentile of PM2.5 level increased most compared to September 2020 from September 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.month == 9] data = data[(data[""Timestamp""].dt.year == 2020) | (data[""Timestamp""].dt.year == 2019)] data[""year""] = data[""Timestamp""].dt.year data = data.groupby([""city"", ""year""])[""PM2.5""].quantile(0.75).reset_index() data = data.pivot(index=""city"", columns=""year"", values=""PM2.5"") data[""diff""] = data[2020] - data[2019] data = data.dropna(subset=""diff"") data = data.sort_values(by=""diff"") print(data.iloc[-1].name) true_code() ","Comparing September 2019 with September 2020, which city experienced the largest increase in its 75th percentile PM2.5 level?",Agra 5347,6683,spatio_temporal_aggregation,How many stations never recorded PM10 in year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] count1 = data['station'].nunique() data = data.dropna(subset=""PM10"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What quantity of stations did not record PM10 in the year 2021?,245 5348,6684,spatio_temporal_aggregation,How many stations never recorded PM10 in year 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] count1 = data['station'].nunique() data = data.dropna(subset=""PM10"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What is the number of stations that never logged PM10 in the year 2022?,169 5349,6685,spatio_temporal_aggregation,How many stations never recorded PM2.5 in year 2017 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] count1 = data['station'].nunique() data = data.dropna(subset=""PM2.5"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What count of stations did not register PM2.5 in the year 2017?,478 5350,6686,spatio_temporal_aggregation,How many stations never recorded PM2.5 in year 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] count1 = data['station'].nunique() data = data.dropna(subset=""PM2.5"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What quantity of stations never noted PM2.5 in the year 2023?,40 5351,6687,spatio_temporal_aggregation,How many stations never recorded PM10 in year 2019 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] count1 = data['station'].nunique() data = data.dropna(subset=""PM10"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What is the number of stations that did not log PM10 in the year 2019?,380 5352,6688,spatio_temporal_aggregation,How many stations never recorded PM2.5 in year 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] count1 = data['station'].nunique() data = data.dropna(subset=""PM2.5"") count2 = data['station'].nunique() count = count1 - count2 print(count) true_code() ",What count of stations never registered PM2.5 in the year 2021?,242 5353,6689,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed 90 µg/m³ of PM2.5 in the year 2019?,25 5354,6690,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations exceeded the Indian guideline for PM10 in 2022?,20 5355,6691,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh went above 90 µg/m³ of PM10 in the year 2019?,19 5356,6692,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations surpassed the WHO guideline for PM2.5 in 2023?,28 5357,6693,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra exceeded 90 µg/m³ of PM10 in the year 2023?,90 5358,6694,spatio_temporal_aggregation,How many stations of West Bengal crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations went above 30 µg/m³ of PM2.5 in 2018?,6 5359,6695,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar surpassed 30 µg/m³ of PM2.5 in the year 2019?,6 5360,6696,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations exceeded 45 µg/m³ of PM2.5 in 2019?,6 5361,6697,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh went above the Indian guideline for PM10 in the year 2017?,5 5362,6698,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Bihar stations surpassed 30 µg/m³ of PM2.5 in 2018?,3 5363,6700,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations went above the Indian guideline for PM10 in 2018?,8 5364,6701,spatio_temporal_aggregation,How many stations of West Bengal crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal surpassed the WHO guideline for PM2.5 in the year 2023?,15 5365,6703,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra went above 90 µg/m³ of PM2.5 in the year 2022?,37 5366,6704,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations surpassed 75 µg/m³ of PM10 in 2023?,31 5367,6705,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded the WHO guideline for PM2.5 in the year 2017?,4 5368,6706,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",What number of Bihar stations went above the WHO guideline for PM2.5 in 2021?,31 5369,6709,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above 30 µg/m³ of PM2.5 in the year 2020?,11 5370,6711,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar exceeded 45 µg/m³ of PM10 in the year 2022?,31 5371,6712,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations went above 45 µg/m³ of PM10 in 2020?,13 5372,6713,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed 90 µg/m³ of PM10 in the year 2017?,5 5373,6716,spatio_temporal_aggregation,How many stations of Bihar crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations surpassed 90 µg/m³ of PM10 in 2021?,28 5374,6719,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh surpassed 90 µg/m³ of PM2.5 in the year 2022?,17 5375,6720,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What count of Maharashtra stations exceeded 90 µg/m³ of PM10 in 2018?,9 5376,6721,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal went above 90 µg/m³ of PM2.5 in the year 2020?,13 5377,6722,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations surpassed 30 µg/m³ of PM2.5 in 2020?,16 5378,6723,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar exceeded the WHO guideline for PM10 in the year 2022?,31 5379,6724,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations went above the Indian guideline for PM2.5 in 2020?,16 5380,6725,spatio_temporal_aggregation,How many stations of West Bengal crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal surpassed the WHO guideline for PM10 in the year 2020?,13 5381,6727,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal went above 45 µg/m³ of PM10 in the year 2019?,13 5382,6728,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations surpassed 75 µg/m³ of PM2.5 in 2021?,31 5383,6730,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations went above 45 µg/m³ of PM2.5 in 2018?,8 5384,6731,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed 90 µg/m³ of PM10 in the year 2023?,53 5385,6732,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations exceeded the Indian guideline for PM10 in 2017?,5 5386,6735,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal exceeded 90 µg/m³ of PM2.5 in the year 2019?,12 5387,6736,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations went above 75 µg/m³ of PM10 in 2021?,16 5388,6737,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar surpassed the WHO guideline for PM2.5 in the year 2023?,34 5389,6738,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Uttar Pradesh stations exceeded 45 µg/m³ of PM2.5 in 2017?,11 5390,6739,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal went above 45 µg/m³ of PM2.5 in the year 2018?,6 5391,6741,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh exceeded the Indian guideline for PM2.5 in the year 2017?,11 5392,6742,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations went above 45 µg/m³ of PM2.5 in 2017?,4 5393,6743,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the WHO guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh surpassed the WHO guideline for PM10 in the year 2023?,28 5394,6745,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh went above 30 µg/m³ of PM10 in the year 2021?,45 5395,6749,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed 90 µg/m³ of PM10 in the year 2022?,38 5396,6750,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations exceeded 30 µg/m³ of PM10 in 2018?,8 5397,6752,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations surpassed 45 µg/m³ of PM2.5 in 2023?,27 5398,6753,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar exceeded 75 µg/m³ of PM10 in the year 2019?,3 5399,6754,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations went above the WHO guideline for PM2.5 in 2023?,89 5400,6755,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed 90 µg/m³ of PM2.5 in the year 2021?,36 5401,6756,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations exceeded 30 µg/m³ of PM10 in 2017?,0 5402,6757,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal went above 75 µg/m³ of PM10 in the year 2019?,13 5403,6758,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Uttar Pradesh stations surpassed 30 µg/m³ of PM2.5 in 2023?,54 5404,6759,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the Indian guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded the Indian guideline for PM2.5 in the year 2019?,13 5405,6760,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations went above the Indian guideline for PM2.5 in 2023?,34 5406,6761,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed 90 µg/m³ of PM10 in the year 2021?,39 5407,6762,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of Bihar stations exceeded the Indian guideline for PM10 in 2021?,28 5408,6763,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above 75 µg/m³ of PM2.5 in the year 2017?,3 5409,6764,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Maharashtra stations surpassed 45 µg/m³ of PM10 in 2017?,10 5410,6766,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations went above the Indian guideline for PM10 in 2018?,7 5411,6767,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed 45 µg/m³ of PM10 in the year 2023?,90 5412,6768,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations exceeded 45 µg/m³ of PM10 in 2023?,15 5413,6769,spatio_temporal_aggregation,How many stations of Bihar crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above 90 µg/m³ of PM10 in the year 2017?,0 5414,6770,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations surpassed 75 µg/m³ of PM10 in 2023?,90 5415,6771,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded 75 µg/m³ of PM2.5 in the year 2017?,4 5416,6772,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations went above 45 µg/m³ of PM2.5 in 2017?,3 5417,6773,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed the WHO guideline for PM2.5 in the year 2018?,8 5418,6774,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations exceeded 30 µg/m³ of PM2.5 in 2019?,20 5419,6776,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Madhya Pradesh stations surpassed 75 µg/m³ of PM2.5 in 2018?,8 5420,6777,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded 45 µg/m³ of PM10 in the year 2021?,16 5421,6778,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations went above 30 µg/m³ of PM2.5 in 2023?,89 5422,6779,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal surpassed the Indian guideline for PM10 in the year 2022?,13 5423,6780,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations exceeded the Indian guideline for PM10 in 2019?,13 5424,6781,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra went above the WHO guideline for PM2.5 in the year 2021?,38 5425,6782,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations surpassed 75 µg/m³ of PM2.5 in 2020?,15 5426,6783,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh exceeded the Indian guideline for PM10 in the year 2020?,21 5427,6784,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",What count of Uttar Pradesh stations went above the WHO guideline for PM2.5 in 2017?,11 5428,6785,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed 45 µg/m³ of PM10 in the year 2017?,5 5429,6786,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations exceeded 30 µg/m³ of PM10 in 2021?,39 5430,6787,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above 30 µg/m³ of PM10 in the year 2023?,31 5431,6788,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of Maharashtra stations surpassed the Indian guideline for PM2.5 in 2023?,89 5432,6789,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh exceeded 90 µg/m³ of PM2.5 in the year 2021?,45 5433,6793,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above the Indian guideline for PM2.5 in the year 2022?,34 5434,6794,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations surpassed the Indian guideline for PM2.5 in 2017?,0 5435,6795,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal exceeded 45 µg/m³ of PM2.5 in the year 2017?,0 5436,6796,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations went above the Indian guideline for PM10 in 2023?,15 5437,6797,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed the Indian guideline for PM2.5 in the year 2018?,16 5438,6798,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations exceeded 75 µg/m³ of PM2.5 in 2020?,13 5439,6799,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh went above the Indian guideline for PM10 in the year 2019?,13 5440,6800,spatio_temporal_aggregation,How many stations of West Bengal crossed the Indian guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations surpassed the Indian guideline for PM2.5 in 2018?,6 5441,6801,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh exceeded 75 µg/m³ of PM10 in the year 2018?,10 5442,6803,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar surpassed 30 µg/m³ of PM2.5 in the year 2022?,34 5443,6805,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal went above 75 µg/m³ of PM2.5 in the year 2022?,13 5444,6806,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations surpassed 45 µg/m³ of PM10 in 2022?,38 5445,6807,spatio_temporal_aggregation,How many stations of Bihar crossed the 75 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar exceeded 75 µg/m³ of PM2.5 in the year 2023?,34 5446,6808,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",What count of Maharashtra stations went above 90 µg/m³ of PM2.5 in 2017?,9 5447,6809,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar surpassed the WHO guideline for PM2.5 in the year 2017?,3 5448,6810,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations exceeded 45 µg/m³ of PM2.5 in 2020?,16 5449,6811,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra went above the Indian guideline for PM10 in the year 2023?,90 5450,6812,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations surpassed 45 µg/m³ of PM10 in 2018?,7 5451,6813,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra exceeded 30 µg/m³ of PM10 in the year 2018?,9 5452,6814,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations went above 90 µg/m³ of PM10 in 2022?,13 5453,6815,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh surpassed 90 µg/m³ of PM2.5 in the year 2017?,4 5454,6816,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Uttar Pradesh stations exceeded 75 µg/m³ of PM2.5 in 2019?,25 5455,6817,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above the Indian guideline for PM10 in the year 2023?,31 5456,6818,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations surpassed 90 µg/m³ of PM10 in 2018?,7 5457,6819,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded 75 µg/m³ of PM10 in the year 2018?,8 5458,6820,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations went above 90 µg/m³ of PM10 in 2021?,13 5459,6821,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal surpassed 90 µg/m³ of PM2.5 in the year 2021?,13 5460,6822,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations exceeded 75 µg/m³ of PM2.5 in 2019?,20 5461,6823,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh went above the WHO guideline for PM10 in the year 2019?,19 5462,6825,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh exceeded 30 µg/m³ of PM2.5 in the year 2022?,56 5463,6827,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar surpassed the Indian guideline for PM10 in the year 2019?,3 5464,6828,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",What count of Uttar Pradesh stations exceeded 75 µg/m³ of PM10 in 2021?,45 5465,6829,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh went above 45 µg/m³ of PM10 in the year 2018?,8 5466,6830,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of Uttar Pradesh stations surpassed the Indian guideline for PM2.5 in 2020?,25 5467,6831,spatio_temporal_aggregation,How many stations of West Bengal crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in West Bengal exceeded 30 µg/m³ of PM2.5 in the year 2020?,13 5468,6832,spatio_temporal_aggregation,How many stations of Bihar crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations went above the WHO guideline for PM10 in 2019?,3 5469,6834,spatio_temporal_aggregation,How many stations of West Bengal crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations exceeded 75 µg/m³ of PM2.5 in 2018?,6 5470,6835,spatio_temporal_aggregation,How many stations of Bihar crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar went above 90 µg/m³ of PM10 in the year 2019?,3 5471,6836,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Uttar Pradesh stations surpassed 45 µg/m³ of PM10 in 2019?,19 5472,6837,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded 30 µg/m³ of PM2.5 in the year 2018?,8 5473,6840,spatio_temporal_aggregation,How many stations of Bihar crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations exceeded 30 µg/m³ of PM10 in 2020?,8 5474,6842,spatio_temporal_aggregation,How many stations of West Bengal crossed the 90 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations surpassed 90 µg/m³ of PM10 in 2020?,13 5475,6843,spatio_temporal_aggregation,How many stations of Bihar crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Bihar exceeded the Indian guideline for PM10 in the year 2022?,31 5476,6844,spatio_temporal_aggregation,How many stations of Bihar crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What count of Bihar stations went above 45 µg/m³ of PM2.5 in 2023?,34 5477,6845,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh surpassed 75 µg/m³ of PM10 in the year 2022?,55 5478,6846,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations exceeded 45 µg/m³ of PM2.5 in 2023?,15 5479,6847,spatio_temporal_aggregation,How many stations of Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Uttar Pradesh went above 90 µg/m³ of PM2.5 in the year 2022?,56 5480,6848,spatio_temporal_aggregation,How many stations of West Bengal crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",What count of West Bengal stations surpassed the WHO guideline for PM10 in 2022?,13 5481,6849,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra exceeded the WHO guideline for PM10 in the year 2017?,10 5482,6850,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of Madhya Pradesh stations went above 45 µg/m³ of PM2.5 in 2019?,13 5483,6851,spatio_temporal_aggregation,How many stations of Maharashtra crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed 75 µg/m³ of PM10 in the year 2022?,38 5484,6854,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['station'].nunique() print(count) true_code() ",What number of Maharashtra stations surpassed the Indian guideline for PM2.5 in 2022?,38 5485,6855,spatio_temporal_aggregation,How many stations of Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['station'].nunique() print(count) true_code() ",How many stations in Madhya Pradesh exceeded 90 µg/m³ of PM10 in the year 2017?,4 5486,6857,spatio_temporal_aggregation,How many stations of Maharashtra crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra surpassed the Indian guideline for PM10 in the year 2022?,38 5487,6858,spatio_temporal_aggregation,How many stations of West Bengal crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['station'].nunique() print(count) true_code() ",What number of West Bengal stations exceeded 45 µg/m³ of PM2.5 in 2020?,13 5488,6859,spatio_temporal_aggregation,How many stations of Maharashtra crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['station'].nunique() print(count) true_code() ",How many stations in Maharashtra went above the WHO guideline for PM2.5 in the year 2017?,10 5489,6860,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 25th percentile of PM2.5 in the Summer season of 2022?,Sikkim 5490,6863,spatio_temporal_aggregation,Which station had the highest average PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the peak average PM10 during the Summer season of 2018?,"Zero Point GICI, Gangtok - SSPCB" 5491,6864,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum average PM10 in the Post-Monsoon season of 2018?,Vrindavan 5492,6865,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?,Tripura 5493,6866,spatio_temporal_aggregation,Which state had the highest average PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak average PM10 in the Monsoon season of 2022?,Jharkhand 5494,6867,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum average PM10 during the Monsoon season of 2024?,Rohtak 5495,6868,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile of PM10 in the Post-Monsoon season of 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5496,6869,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum average PM2.5 during the Post-Monsoon season of 2020?,Kerala 5497,6870,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum average PM2.5 in the Summer season of 2022?,Sikkim 5498,6871,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak average PM2.5 during the Winter season of 2019?,"Zero Point GICI, Gangtok - SSPCB" 5499,6872,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM2.5 in the Post-Monsoon season of 2023?,Tripura 5500,6873,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum median PM2.5 during the Post-Monsoon season of 2023?,Silchar 5501,6874,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average PM2.5 in the Winter season of 2019?,"Vyttila, Kochi - Kerala PCB" 5502,6875,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum 25th percentile of PM10 during the Monsoon season of 2020?,Tripura 5503,6876,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd highest 25th percentile of PM2.5 in the Post-Monsoon season of 2019?,Sikkim 5504,6877,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak 25th percentile of PM10 during the Monsoon season of 2024?,Thanjavur 5505,6878,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd maximum 25th percentile of PM2.5 in the Summer season of 2021?,Virudhunagar 5506,6879,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 25th percentile of PM10 during the Summer season of 2018?,Thiruvananthapuram 5507,6880,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM10 in the Summer season of 2024?,Manipur 5508,6881,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum 25th percentile of PM10 during the Summer season of 2024?,"Velippalayam, Nagapattinam - TNPCB" 5509,6882,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd highest 25th percentile of PM10 in the Winter season of 2018?,Tripura 5510,6885,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station noted the 2nd maximum median PM10 during the Summer season of 2022?,"Vikas Sadan, Gurugram - HSPCB" 5511,6886,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 25th percentile of PM10 in the Monsoon season of 2022?,Madikeri 5512,6888,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd lowest 25th percentile of PM10 in the Post-Monsoon season of 2022?,Arunachal Pradesh 5513,6889,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2019?,"Tamaka Ind. Area, Kolar - KSPCB" 5514,6890,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum median PM10 in the Winter season of 2018?,Kerala 5515,6891,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum average PM10 during the Winter season of 2022?,Mizoram 5516,6892,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median PM2.5 in the Post-Monsoon season of 2022?,Kolar 5517,6893,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd maximum median PM2.5 during the Monsoon season of 2018?,Vrindavan 5518,6894,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest 75th percentile of PM10 in the Summer season of 2018?,Karnataka 5519,6896,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM2.5 in the Summer season of 2021?,Manipur 5520,6897,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum 25th percentile of PM10 during the Summer season of 2022?,Himachal Pradesh 5521,6898,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak 75th percentile of PM2.5 during the Summer season of 2023?,"Vijay Nagar, Sangli - MPCB" 5522,6900,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak 75th percentile of PM10 during the Monsoon season of 2024?,"Vyttila, Kochi - Kerala PCB" 5523,6901,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM10 in the Summer season of 2023?,Ulhasnagar 5524,6902,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 75th percentile of PM2.5 in the Winter season of 2021?,Meghalaya 5525,6903,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum average PM2.5 during the Winter season of 2021?,Vijayawada 5526,6904,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM10 in the Monsoon season of 2024?,Manipur 5527,6905,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM2.5 during the Winter season of 2019?,Uttarakhand 5528,6908,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd maximum 25th percentile of PM10 in the Post-Monsoon season of 2023?,Tiruchirappalli 5529,6909,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the peak 25th percentile of PM10 during the Monsoon season of 2018?,Yamuna Nagar 5530,6910,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM2.5 in the Post-Monsoon season of 2024?,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 5531,6911,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 75th percentile of PM10 during the Post-Monsoon season of 2022?,"Brahmagiri, Udupi - KSPCB" 5532,6912,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station noted the 3rd maximum 25th percentile of PM10 in the Summer season of 2018?,"Worli, Mumbai - MPCB" 5533,6913,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 5534,6914,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum average PM10 in the Post-Monsoon season of 2024?,Sikkim 5535,6915,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 75th percentile of PM2.5 during the Summer season of 2019?,Mumbai 5536,6916,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest average PM2.5 in the Monsoon season of 2023?,"Zero Point GICI, Gangtok - SSPCB" 5537,6917,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2023?,Tripura 5538,6918,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum average PM10 in the Monsoon season of 2018?,Kerala 5539,6919,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak 75th percentile of PM10 during the Post-Monsoon season of 2021?,Uttarakhand 5540,6920,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum 25th percentile of PM10 in the Monsoon season of 2023?,Delhi 5541,6921,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum 25th percentile of PM2.5 during the Post-Monsoon season of 2020?,Meghalaya 5542,6922,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum median PM2.5 in the Summer season of 2018?,"Kendriya Vidyalaya, Lucknow - CPCB" 5543,6923,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd lowest 75th percentile of PM10 during the Winter season of 2018?,Tirupati 5544,6924,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 75th percentile of PM10 in the Winter season of 2023?,Madikeri 5545,6925,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2020?,Puducherry 5546,6927,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum median PM10 during the Summer season of 2022?,Meghalaya 5547,6929,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 75th percentile of PM2.5 in the Summer season of 2018?,Kerala 5548,6930,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum median PM2.5 during the Winter season of 2024?,Sikkim 5549,6931,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak 75th percentile of PM10 during the Post-Monsoon season of 2020?,Vrindavan 5550,6932,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest median PM10 in the Winter season of 2020?,"Lumpyngngad, Shillong - Meghalaya PCB" 5551,6934,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak median PM2.5 in the Post-Monsoon season of 2024?,Delhi 5552,6937,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 75th percentile of PM2.5 during the Summer season of 2019?,Vrindavan 5553,6939,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 25th percentile of PM2.5 during the Monsoon season of 2021?,Uttarakhand 5554,6940,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM10 in the Monsoon season of 2020?,Meghalaya 5555,6941,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum average PM2.5 during the Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 5556,6943,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 25th percentile of PM10 during the Post-Monsoon season of 2023?,"Tarapur, Silchar - PCBA" 5557,6944,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum median PM10 in the Summer season of 2020?,Tamil Nadu 5558,6945,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 25th percentile of PM2.5 during the Summer season of 2021?,Uttarakhand 5559,6946,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in the Post-Monsoon season of 2018?,Yadgir 5560,6947,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 25th percentile of PM2.5 in the Summer season of 2024?,Puducherry 5561,6948,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum 25th percentile of PM10 during the Winter season of 2020?,Mizoram 5562,6949,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest median PM2.5 in the Winter season of 2020?,"Vijay Nagar, Sangli - MPCB" 5563,6950,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest 75th percentile of PM10 during the Summer season of 2021?,"Udyogamandal, Eloor - Kerala PCB" 5564,6951,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM2.5 in the Summer season of 2020?,Uttarakhand 5565,6952,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile of PM2.5 during the Monsoon season of 2019?,Chandigarh 5566,6954,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum average PM10 during the Post-Monsoon season of 2019?,Virar 5567,6955,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak 75th percentile of PM10 in the Post-Monsoon season of 2018?,"Zero Point GICI, Gangtok - SSPCB" 5568,6957,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 75th percentile of PM10 in the Summer season of 2018?,Kolkata 5569,6958,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 25th percentile of PM2.5 during the Summer season of 2020?,Eloor 5570,6959,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak 75th percentile of PM10 in the Summer season of 2024?,Tirunelveli 5571,6960,spatio_temporal_aggregation,Which state had the highest median PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak median PM10 during the Winter season of 2020?,Uttarakhand 5572,6961,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 75th percentile of PM2.5 in the Summer season of 2021?,"Devaraj Urs Badavane, Davanagere - KSPCB" 5573,6962,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd minimum 25th percentile of PM10 during the Summer season of 2019?,Chennai 5574,6963,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 75th percentile of PM10 in the Post-Monsoon season of 2018?,Thiruvananthapuram 5575,6964,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the minimum median PM2.5 during the Post-Monsoon season of 2020?,Mizoram 5576,6965,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd maximum median PM10 in the Winter season of 2022?,Himachal Pradesh 5577,6967,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM10 in the Winter season of 2018?,"Tamaka Ind. Area, Kolar - KSPCB" 5578,6968,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum median PM10 during the Winter season of 2024?,"Manipur University, Imphal - Manipur PCB" 5579,6969,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum 25th percentile of PM2.5 in the Post-Monsoon season of 2021?,Virar 5580,6970,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 25th percentile of PM2.5 during the Winter season of 2022?,Meghalaya 5581,6972,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station noted the 3rd maximum average PM2.5 during the Monsoon season of 2023?,"VOC Nagar_SIPCOT, Ranipet - TNPCB" 5582,6973,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak average PM2.5 in the Post-Monsoon season of 2021?,Uttarakhand 5583,6974,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum median PM2.5 during the Winter season of 2024?,Satna 5584,6975,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak average PM2.5 in the Post-Monsoon season of 2020?,"Zero Point GICI, Gangtok - SSPCB" 5585,6977,spatio_temporal_aggregation,Which state had the highest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak average PM10 in the Post-Monsoon season of 2018?,Uttarakhand 5586,6979,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 25th percentile of PM2.5 in the Summer season of 2023?,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 5587,6981,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station noted the minimum 25th percentile of PM10 in the Post-Monsoon season of 2022?,"Zero Point GICI, Gangtok - SSPCB" 5588,6982,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM2.5 during the Monsoon season of 2019?,Jharkhand 5589,6984,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 25th percentile of PM2.5 during the Summer season of 2020?,Aizawl 5590,6985,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM10 in the Monsoon season of 2021?,"Vikas Sadan, Gurugram - HSPCB" 5591,6986,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum 75th percentile of PM2.5 during the Monsoon season of 2018?,"Worli, Mumbai - MPCB" 5592,6987,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station noted the 3rd highest 75th percentile of PM2.5 in the Monsoon season of 2021?,"Vijay Nagar, Sangli - MPCB" 5593,6988,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2024?,Pathardih 5594,6989,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd maximum 25th percentile of PM2.5 in the Monsoon season of 2022?,Vijayawada 5595,6990,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 75th percentile of PM10 during the Monsoon season of 2020?,Uttarakhand 5596,6991,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile of PM10 in the Monsoon season of 2019?,Chamarajanagar 5597,6992,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum 25th percentile of PM10 during the Monsoon season of 2023?,Silchar 5598,6993,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM2.5 in the Winter season of 2019?,Uttarakhand 5599,6994,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak 75th percentile of PM10 during the Monsoon season of 2024?,Thanjavur 5600,6995,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 75th percentile of PM10 in the Post-Monsoon season of 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 5601,6996,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum 25th percentile of PM10 during the Summer season of 2020?,Meghalaya 5602,6997,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum average PM10 in the Post-Monsoon season of 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 5603,6998,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum 75th percentile of PM10 during the Monsoon season of 2019?,"Vyttila, Kochi - Kerala PCB" 5604,6999,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum 25th percentile of PM2.5 in the Post-Monsoon season of 2019?,Meghalaya 5605,7001,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum 25th percentile of PM10 in the Monsoon season of 2023?,"Velippalayam, Nagapattinam - TNPCB" 5606,7002,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd highest median PM10 during the Post-Monsoon season of 2019?,Virudhunagar 5607,7004,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak average PM2.5 during the Winter season of 2023?,Virudhunagar 5608,7005,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum average PM2.5 in the Monsoon season of 2021?,Mizoram 5609,7006,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest average PM10 during the Summer season of 2020?,Sikkim 5610,7007,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd lowest average PM2.5 in the Winter season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5611,7008,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum median PM2.5 during the Summer season of 2019?,Tripura 5612,7009,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2022?,Himachal Pradesh 5613,7010,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 25th percentile of PM10 in the Summer season of 2024?,Puducherry 5614,7011,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station noted the minimum average PM2.5 during the Monsoon season of 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5615,7014,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd lowest 75th percentile of PM10 in the Winter season of 2022?,Nandesari 5616,7015,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest average PM10 during the Post-Monsoon season of 2019?,Kolar 5617,7016,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum median PM10 in the Post-Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 5618,7017,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 75th percentile of PM2.5 during the Monsoon season of 2020?,Hubballi 5619,7018,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM2.5 in the Post-Monsoon season of 2019?,Tripura 5620,7019,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum 25th percentile of PM10 during the Summer season of 2024?,Sikkim 5621,7021,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in the Post-Monsoon season of 2021?,Virar 5622,7022,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum 75th percentile of PM10 during the Summer season of 2024?,Varanasi 5623,7023,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 25th percentile of PM10 in the Winter season of 2024?,Tripura 5624,7024,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 75th percentile of PM2.5 during the Monsoon season of 2023?,"Tarapur, Silchar - PCBA" 5625,7025,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak 75th percentile of PM2.5 in the Winter season of 2021?,Uttarakhand 5626,7026,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station noted the 3rd maximum 25th percentile of PM2.5 during the Post-Monsoon season of 2022?,"Vijay Nagar Scheme-78, Indore - Glenmark" 5627,7027,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest average PM10 in the Post-Monsoon season of 2021?,Sikkim 5628,7029,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station noted the 2nd lowest 25th percentile of PM10 in the Winter season of 2024?,"GIDC, Nandesari - Nandesari Ind. Association" 5629,7030,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum median PM2.5 during the Winter season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5630,7031,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd minimum median PM10 in the Monsoon season of 2023?,Koppal 5631,7032,spatio_temporal_aggregation,Which station had the highest median PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak median PM10 during the Monsoon season of 2022?,"Yerramukkapalli, Kadapa - APPCB" 5632,7033,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median PM10 in the Post-Monsoon season of 2023?,"Vijay Nagar Scheme-78, Indore - Glenmark" 5633,7034,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum average PM10 during the Monsoon season of 2018?,Maharashtra 5634,7035,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station noted the minimum median PM2.5 in the Winter season of 2018?,"Bandhavgar Colony, Satna - Birla Cement" 5635,7036,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum median PM10 during the Post-Monsoon season of 2019?,Shillong 5636,7038,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum average PM2.5 during the Post-Monsoon season of 2024?,Mizoram 5637,7039,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM10 in the Monsoon season of 2018?,Siliguri 5638,7040,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum average PM2.5 during the Winter season of 2024?,Mizoram 5639,7042,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest average PM10 during the Summer season of 2024?,"Crescent University, Chengalpattu - TNPCB" 5640,7043,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM2.5 in the Summer season of 2022?,Jharkhand 5641,7044,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum median PM2.5 during the Post-Monsoon season of 2019?,Virudhunagar 5642,7045,spatio_temporal_aggregation,Which city had the highest average PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak average PM10 in the Monsoon season of 2023?,Virudhunagar 5643,7046,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum average PM2.5 during the Summer season of 2023?,"Velippalayam, Nagapattinam - TNPCB" 5644,7047,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak median PM2.5 in the Winter season of 2020?,"Zero Point GICI, Gangtok - SSPCB" 5645,7048,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 75th percentile of PM2.5 during the Winter season of 2022?,Delhi 5646,7049,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum average PM10 in the Post-Monsoon season of 2023?,"Zero Point GICI, Gangtok - SSPCB" 5647,7050,spatio_temporal_aggregation,Which station had the highest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak median PM10 during the Winter season of 2019?,"Zero Point GICI, Gangtok - SSPCB" 5648,7051,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM2.5 in the Monsoon season of 2023?,Tirupur 5649,7052,spatio_temporal_aggregation,Which city had the highest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak median PM10 during the Post-Monsoon season of 2019?,Vrindavan 5650,7053,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum median PM10 in the Winter season of 2018?,Tirupati 5651,7054,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM2.5 during the Summer season of 2023?,Puducherry 5652,7055,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum 25th percentile of PM10 in the Summer season of 2018?,"Yerramukkapalli, Kadapa - APPCB" 5653,7056,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 75th percentile of PM10 during the Post-Monsoon season of 2021?,Udupi 5654,7057,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest average PM10 in the Winter season of 2022?,Vijayawada 5655,7058,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak 75th percentile of PM2.5 during the Monsoon season of 2024?,Thoothukudi 5656,7059,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station noted the 3rd maximum average PM10 in the Summer season of 2021?,"Vikas Sadan, Gurugram - HSPCB" 5657,7060,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 25th percentile of PM10 during the Winter season of 2021?,"Vikas Sadan, Gurugram - HSPCB" 5658,7063,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in the Summer season of 2021?,Virudhunagar 5659,7064,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 75th percentile of PM2.5 in the Summer season of 2021?,Meghalaya 5660,7065,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station noted the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2018?,"Plammoodu, Thiruvananthapuram - Kerala PCB" 5661,7067,spatio_temporal_aggregation,Which state had the highest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM10 during the Post-Monsoon season of 2020?,Uttarakhand 5662,7068,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum median PM2.5 in the Monsoon season of 2024?,Tirupur 5663,7069,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest 75th percentile of PM10 during the Summer season of 2018?,Vijayawada 5664,7072,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile of PM10 in the Monsoon season of 2023?,Silchar 5665,7073,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum 25th percentile of PM10 during the Summer season of 2019?,Andhra Pradesh 5666,7074,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd lowest median PM10 in the Summer season of 2023?,Damoh 5667,7077,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum average PM10 during the Summer season of 2020?,Virudhunagar 5668,7078,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest median PM2.5 in the Monsoon season of 2022?,Sikkim 5669,7079,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 25th percentile of PM10 during the Summer season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 5670,7080,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station noted the 2nd maximum 25th percentile of PM2.5 in the Summer season of 2019?,"Yerramukkapalli, Kadapa - APPCB" 5671,7081,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile of PM2.5 during the Winter season of 2018?,Satna 5672,7082,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak 25th percentile of PM2.5 in the Monsoon season of 2018?,Yamuna Nagar 5673,7084,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM2.5 in the Post-Monsoon season of 2020?,Virar 5674,7085,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM2.5 during the Post-Monsoon season of 2020?,Uttarakhand 5675,7086,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station noted the minimum 75th percentile of PM2.5 in the Post-Monsoon season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5676,7088,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd maximum average PM10 in the Summer season of 2022?,Bihar 5677,7089,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest 75th percentile of PM10 during the Summer season of 2023?,Puducherry 5678,7091,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd minimum average PM2.5 during the Summer season of 2021?,Koppal 5679,7092,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 75th percentile of PM2.5 in the Summer season of 2023?,Jharkhand 5680,7093,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM10 during the Summer season of 2022?,Meghalaya 5681,7094,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum 75th percentile of PM2.5 during the Monsoon season of 2023?,Meghalaya 5682,7097,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd maximum median PM2.5 in the Summer season of 2022?,Haryana 5683,7099,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM2.5 in the Monsoon season of 2024?,Delhi 5684,7100,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum average PM2.5 during the Summer season of 2018?,"Kendriya Vidyalaya, Lucknow - CPCB" 5685,7101,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 25th percentile of PM2.5 in the Post-Monsoon season of 2023?,Silchar 5686,7102,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest median PM2.5 during the Summer season of 2018?,Andhra Pradesh 5687,7103,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak average PM2.5 in the Monsoon season of 2021?,Virudhunagar 5688,7104,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station noted the 2nd minimum 75th percentile of PM2.5 during the Post-Monsoon season of 2024?,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 5689,7105,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest median PM10 in the Winter season of 2020?,Virudhunagar 5690,7106,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 75th percentile of PM2.5 in the Monsoon season of 2020?,Mizoram 5691,7108,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM10 in the Post-Monsoon season of 2021?,Puducherry 5692,7109,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum average PM10 during the Monsoon season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5693,7110,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state noted the minimum average PM10 in the Monsoon season of 2019?,Kerala 5694,7111,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak 75th percentile of PM2.5 during the Winter season of 2020?,"Zero Point GICI, Gangtok - SSPCB" 5695,7112,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum 75th percentile of PM10 in the Summer season of 2021?,"Vikas Sadan, Gurugram - HSPCB" 5696,7113,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak average PM2.5 during the Winter season of 2021?,"Zero Point GICI, Gangtok - SSPCB" 5697,7116,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station noted the 2nd minimum 25th percentile of PM2.5 in the Summer season of 2020?,"Vasai West, Mumbai - MPCB" 5698,7117,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the minimum median PM10 during the Summer season of 2019?,Tamil Nadu 5699,7118,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 75th percentile of PM2.5 in the Summer season of 2022?,"ECIL Kapra, Hyderabad - TSPCB" 5700,7119,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum average PM10 during the Monsoon season of 2019?,Meghalaya 5701,7120,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM10 in the Winter season of 2024?,Vijayapura 5702,7121,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum average PM2.5 in the Summer season of 2020?,Chandigarh 5703,7123,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile of PM10 in the Winter season of 2024?,"Ibrahimpur, Vijayapura - KSPCB" 5704,7125,spatio_temporal_aggregation,Which city had the highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the peak median PM10 in the Post-Monsoon season of 2021?,Virudhunagar 5705,7127,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum average PM2.5 in the Monsoon season of 2024?,Meghalaya 5706,7128,spatio_temporal_aggregation,Which state had the highest average PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM10 during the Summer season of 2020?,Uttarakhand 5707,7129,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest median PM2.5 in the Winter season of 2023?,Silchar 5708,7131,spatio_temporal_aggregation,Which state had the highest average PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM10 in the Winter season of 2022?,Bihar 5709,7132,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest average PM10 during the Winter season of 2020?,Meghalaya 5710,7134,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum median PM10 during the Post-Monsoon season of 2022?,Vijayawada 5711,7135,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest average PM2.5 in the Post-Monsoon season of 2018?,Tripura 5712,7136,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum median PM2.5 during the Summer season of 2024?,"Bhelupur, Varanasi - UPPCB" 5713,7137,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd minimum 75th percentile of PM10 in the Winter season of 2023?,Chamarajanagar 5714,7138,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the minimum 25th percentile of PM10 during the Post-Monsoon season of 2019?,Meghalaya 5715,7139,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum average PM2.5 in the Summer season of 2022?,Delhi 5716,7142,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station registered the 3rd minimum median PM10 in the Winter season of 2019?,"Urban, Chamarajanagar - KSPCB" 5717,7143,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak 25th percentile of PM2.5 during the Post-Monsoon season of 2019?,"Zero Point GICI, Gangtok - SSPCB" 5718,7145,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 75th percentile of PM2.5 in the Post-Monsoon season of 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5719,7146,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station noted the 3rd minimum 25th percentile of PM2.5 during the Monsoon season of 2020?,"Vasai West, Mumbai - MPCB" 5720,7148,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd minimum average PM2.5 during the Post-Monsoon season of 2023?,"KHB Colony, Karwar - KSPCB" 5721,7149,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station noted the peak 75th percentile of PM2.5 in the Post-Monsoon season of 2021?,"Zero Point GICI, Gangtok - SSPCB" 5722,7150,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?,"Vijay Nagar, Sangli - MPCB" 5723,7153,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM2.5 in the Summer season of 2024?,"IESD Banaras Hindu University, Varanasi - UPPCB" 5724,7155,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd highest average PM10 in the Monsoon season of 2023?,Tiruchirappalli 5725,7156,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 75th percentile of PM10 during the Summer season of 2021?,Puducherry 5726,7157,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd minimum median PM2.5 in the Winter season of 2020?,Satna 5727,7159,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season of 2020?,"Udyogamandal, Eloor - Kerala PCB" 5728,7160,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak 75th percentile of PM2.5 in the Winter season of 2023?,Delhi 5729,7161,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum median PM2.5 during the Winter season of 2024?,Tripura 5730,7162,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 25th percentile of PM10 in the Monsoon season of 2022?,"Zero Point GICI, Gangtok - SSPCB" 5731,7163,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM2.5 in the Monsoon season of 2022?,Jharkhand 5732,7164,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum 75th percentile of PM10 during the Winter season of 2024?,Panchkula 5733,7165,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile of PM10 in the Winter season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5734,7166,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum 25th percentile of PM2.5 during the Winter season of 2021?,Aizawl 5735,7167,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum average PM2.5 in the Summer season of 2022?,Gangtok 5736,7168,spatio_temporal_aggregation,Which station had the highest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak median PM10 in the Post-Monsoon season of 2020?,"Zero Point GICI, Gangtok - SSPCB" 5737,7169,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd minimum average PM2.5 during the Winter season of 2024?,Satna 5738,7170,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum 25th percentile of PM2.5 in the Winter season of 2020?,Puducherry 5739,7171,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest median PM2.5 in the Winter season of 2021?,"Devaraj Urs Badavane, Davanagere - KSPCB" 5740,7173,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd lowest 75th percentile of PM10 in the Monsoon season of 2019?,Kerala 5741,7174,spatio_temporal_aggregation,Which state had the highest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak median PM10 during the Winter season of 2022?,Bihar 5742,7176,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum median PM2.5 during the Post-Monsoon season of 2022?,Delhi 5743,7178,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city registered the 3rd maximum median PM10 in the Summer season of 2019?,Virudhunagar 5744,7179,spatio_temporal_aggregation,Which state had the highest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM10 during the Post-Monsoon season of 2024?,Delhi 5745,7181,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak 25th percentile of PM2.5 in the Summer season of 2024?,Delhi 5746,7183,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest median PM2.5 in the Post-Monsoon season of 2019?,"Hombegowda Nagar, Bengaluru - KSPCB" 5747,7184,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak 25th percentile of PM10 in the Post-Monsoon season of 2022?,Delhi 5748,7185,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd minimum median PM2.5 during the Monsoon season of 2018?,Chikkaballapur 5749,7186,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 25th percentile of PM2.5 during the Summer season of 2019?,Satna 5750,7188,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd highest median PM10 during the Monsoon season of 2020?,Virudhunagar 5751,7189,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile of PM10 in the Post-Monsoon season of 2022?,Himachal Pradesh 5752,7190,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum median PM2.5 in the Post-Monsoon season of 2021?,Sikkim 5753,7192,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM10 in the Winter season of 2023?,"Semmandalam, Cuddalore - TNPCB" 5754,7193,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station registered the peak 25th percentile of PM10 in the Winter season of 2022?,"Yerramukkapalli, Kadapa - APPCB" 5755,7194,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum median PM10 during the Winter season of 2022?,Virar 5756,7195,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest average PM10 in the Summer season of 2020?,Virar 5757,7196,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum average PM10 during the Summer season of 2024?,Puducherry 5758,7197,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd lowest 75th percentile of PM10 in the Summer season of 2022?,Jammu and Kashmir 5759,7198,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile of PM10 in the Winter season of 2019?,"Urban, Chamarajanagar - KSPCB" 5760,7199,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum 25th percentile of PM10 during the Winter season of 2019?,Kerala 5761,7200,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 25th percentile of PM2.5 in the Winter season of 2018?,Satna 5762,7201,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak average PM2.5 during the Winter season of 2022?,Jharkhand 5763,7202,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum 25th percentile of PM2.5 in the Winter season of 2018?,"Bandhavgar Colony, Satna - Birla Cement" 5764,7203,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd maximum 75th percentile of PM10 during the Post-Monsoon season of 2024?,Rohtak 5765,7204,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median PM2.5 in the Winter season of 2019?,Tamil Nadu 5766,7205,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd maximum 75th percentile of PM10 in the Summer season of 2020?,Virudhunagar 5767,7206,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum 25th percentile of PM2.5 during the Post-Monsoon season of 2024?,Manipur 5768,7208,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state registered the 3rd minimum average PM10 during the Winter season of 2019?,Kerala 5769,7209,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum 75th percentile of PM2.5 in the Post-Monsoon season of 2023?,Gangtok 5770,7210,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM2.5 in the Monsoon season of 2024?,Koppal 5771,7211,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum median PM10 in the Post-Monsoon season of 2024?,"Vikas Sadan, Gurugram - HSPCB" 5772,7212,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd minimum median PM2.5 during the Winter season of 2023?,Sikkim 5773,7214,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum average PM10 during the Post-Monsoon season of 2019?,Eloor 5774,7215,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest 75th percentile of PM10 in the Winter season of 2022?,Mizoram 5775,7216,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak 25th percentile of PM2.5 during the Monsoon season of 2019?,"Zero Point GICI, Gangtok - SSPCB" 5776,7217,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum average PM2.5 in the Winter season of 2019?,Tripura 5777,7218,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd minimum 25th percentile of PM2.5 during the Monsoon season of 2019?,Bathinda 5778,7219,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest 25th percentile of PM2.5 in the Post-Monsoon season of 2018?,Sikkim 5779,7222,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak average PM2.5 during the Post-Monsoon season of 2019?,Vrindavan 5780,7223,spatio_temporal_aggregation,Which city had the highest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city registered the peak average PM10 in the Monsoon season of 2024?,Thanjavur 5781,7226,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum 75th percentile of PM2.5 during the Post-Monsoon season of 2021?,"Yerramukkapalli, Kadapa - APPCB" 5782,7227,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 75th percentile of PM10 in the Monsoon season of 2021?,Shillong 5783,7228,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum 75th percentile of PM10 during the Winter season of 2019?,Shillong 5784,7229,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum 75th percentile of PM2.5 in the Monsoon season of 2020?,Tripura 5785,7230,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station noted the minimum 25th percentile of PM10 during the Monsoon season of 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 5786,7231,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the minimum median PM2.5 in the Summer season of 2022?,Aizawl 5787,7233,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum 25th percentile of PM10 during the Winter season of 2019?,Sikkim 5788,7234,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile of PM10 in the Summer season of 2024?,Sikkim 5789,7235,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum average PM2.5 during the Winter season of 2020?,Meghalaya 5790,7236,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum 75th percentile of PM10 in the Monsoon season of 2023?,Rohtak 5791,7237,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM2.5 in the Winter season of 2020?,Sikkim 5792,7238,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd minimum 25th percentile of PM10 during the Summer season of 2018?,Rajamahendravaram 5793,7239,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd highest median PM10 in the Summer season of 2020?,Virudhunagar 5794,7240,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile of PM10 in the Monsoon season of 2022?,"Sector-19A Nerul, Navi Mumbai - IITM" 5795,7241,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city registered the 2nd minimum average PM10 during the Summer season of 2019?,Chennai 5796,7242,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest 25th percentile of PM10 in the Summer season of 2022?,Manipur 5797,7244,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd minimum median PM2.5 in the Monsoon season of 2023?,Gangtok 5798,7245,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum median PM2.5 during the Summer season of 2021?,Manipur 5799,7246,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM10 in the Winter season of 2023?,Panchkula 5800,7247,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum average PM2.5 during the Monsoon season of 2021?,Meghalaya 5801,7249,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest average PM2.5 in the Winter season of 2023?,Arunachal Pradesh 5802,7250,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum average PM2.5 during the Monsoon season of 2024?,Mizoram 5803,7251,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum 25th percentile of PM10 in the Post-Monsoon season of 2020?,Puducherry 5804,7252,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state recorded the 3rd highest average PM10 during the Summer season of 2019?,Sikkim 5805,7253,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state registered the 2nd minimum median PM2.5 in the Post-Monsoon season of 2023?,Sikkim 5806,7254,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station noted the 2nd maximum 75th percentile of PM2.5 during the Monsoon season of 2023?,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 5807,7255,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM10 in the Monsoon season of 2018?,Tripura 5808,7257,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum average PM10 during the Monsoon season of 2022?,Delhi 5809,7259,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state registered the 3rd maximum 25th percentile of PM2.5 in the Winter season of 2021?,Manipur 5810,7260,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station noted the 2nd minimum 25th percentile of PM2.5 during the Monsoon season of 2022?,"Sector-19A Nerul, Navi Mumbai - IITM" 5811,7261,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest median PM10 in the Winter season of 2024?,Manipur 5812,7263,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city noted the 2nd highest 75th percentile of PM2.5 in the Winter season of 2021?,Virar 5813,7264,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 75th percentile of PM2.5 during the Monsoon season of 2019?,"Sector-25, Chandigarh - CPCC" 5814,7265,spatio_temporal_aggregation,Which state had the highest average PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak average PM10 in the Monsoon season of 2023?,Manipur 5815,7266,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak average PM2.5 during the Summer season of 2021?,Uttarakhand 5816,7267,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest median PM2.5 in the Summer season of 2024?,Tiruchirappalli 5817,7268,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum 25th percentile of PM2.5 in the Post-Monsoon season of 2021?,Sikkim 5818,7269,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest 25th percentile of PM10 in the Winter season of 2024?,Mizoram 5819,7270,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM2.5 during the Summer season of 2022?,"Vijay Nagar Scheme-78, Indore - Glenmark" 5820,7271,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd minimum 25th percentile of PM10 in the Post-Monsoon season of 2019?,Thane 5821,7272,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state noted the 3rd maximum 25th percentile of PM10 during the Winter season of 2020?,Puducherry 5822,7273,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the peak 75th percentile of PM2.5 during the Winter season of 2022?,Virudhunagar 5823,7275,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city noted the 3rd maximum average PM2.5 in the Monsoon season of 2024?,Ranipet 5824,7277,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station registered the 2nd maximum 25th percentile of PM2.5 in the Monsoon season of 2020?,"Yerramukkapalli, Kadapa - APPCB" 5825,7278,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd minimum 75th percentile of PM2.5 during the Winter season of 2022?,Bhilai 5826,7279,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile of PM2.5 in the Winter season of 2020?,Virar 5827,7280,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum average PM10 in the Post-Monsoon season of 2023?,Sikkim 5828,7281,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station noted the 2nd maximum 75th percentile of PM2.5 during the Monsoon season of 2020?,"Yerramukkapalli, Kadapa - APPCB" 5829,7283,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum 75th percentile of PM10 during the Monsoon season of 2024?,Sikkim 5830,7284,spatio_temporal_aggregation,Which city had the highest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the peak median PM10 in the Winter season of 2024?,Rohtak 5831,7285,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM10 in the Monsoon season of 2022?,Virar 5832,7287,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 25th percentile of PM10 during the Monsoon season of 2019?,Uttarakhand 5833,7288,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest average PM2.5 in the Monsoon season of 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 5834,7289,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum 25th percentile of PM10 in the Summer season of 2020?,Aizawl 5835,7292,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city registered the minimum 25th percentile of PM2.5 in the Post-Monsoon season of 2018?,Satna 5836,7293,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city noted the minimum 25th percentile of PM10 during the Post-Monsoon season of 2022?,Gangtok 5837,7294,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile of PM10 in the Post-Monsoon season of 2021?,"Brahmagiri, Udupi - KSPCB" 5838,7295,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station registered the minimum median PM2.5 in the Summer season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 5839,7296,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state noted the 2nd maximum 25th percentile of PM10 during the Monsoon season of 2022?,Delhi 5840,7297,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average PM2.5 in the Post-Monsoon season of 2022?,Kolar 5841,7298,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd maximum 25th percentile of PM2.5 in the Summer season of 2022?,Virar 5842,7299,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city noted the 2nd minimum average PM2.5 during the Monsoon season of 2020?,Hubballi 5843,7300,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile of PM10 in the Post-Monsoon season of 2023?,Mizoram 5844,7302,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state noted the 2nd minimum median PM2.5 during the Winter season of 2024?,Mizoram 5845,7303,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile of PM2.5 in the Summer season of 2018?,Tripura 5846,7304,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station registered the 2nd minimum median PM10 in the Post-Monsoon season of 2021?,"Brahmagiri, Udupi - KSPCB" 5847,7305,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest average PM2.5 in the Summer season of 2019?,Tamil Nadu 5848,7306,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile of PM10 during the Summer season of 2024?,"Velippalayam, Nagapattinam - TNPCB" 5849,7307,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state registered the 2nd maximum 75th percentile of PM10 in the Summer season of 2021?,Sikkim 5850,7309,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average PM2.5 in the Monsoon season of 2020?,Virudhunagar 5851,7310,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city registered the 3rd minimum 75th percentile of PM2.5 during the Winter season of 2023?,Chamarajanagar 5852,7311,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd lowest average PM10 in the Winter season of 2022?,Nandesari 5853,7312,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 75th percentile of PM2.5 in the Summer season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 5854,7314,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station noted the 3rd lowest average PM10 in the Monsoon season of 2020?,"Hebbal 1st Stage, Mysuru - KSPCB" 5855,7315,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest 25th percentile of PM10 in the Monsoon season of 2020?,Virudhunagar 5856,7316,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state registered the peak median PM2.5 in the Monsoon season of 2021?,Uttarakhand 5857,7321,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak 75th percentile of PM10 during the Monsoon season of 2021?,Uttarakhand 5858,7322,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station registered the 3rd maximum median PM2.5 in the Post-Monsoon season of 2019?,"Vyttila, Kochi - Kerala PCB" 5859,7324,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the minimum 25th percentile of PM10 during the Monsoon season of 2022?,"Brahmagiri, Udupi - KSPCB" 5860,7326,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city noted the peak average PM2.5 during the Post-Monsoon season of 2020?,Vrindavan 5861,7327,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the peak 75th percentile of PM10 during the Summer season of 2024?,Delhi 5862,7328,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city registered the 2nd maximum 75th percentile of PM2.5 in the Winter season of 2024?,Kozhikode 5863,7329,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state noted the 3rd lowest average PM10 in the Winter season of 2018?,Andhra Pradesh 5864,7330,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest median PM2.5 in the Post-Monsoon season of 2018?,Tripura 5865,7332,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city noted the 3rd minimum 25th percentile of PM2.5 during the Winter season of 2023?,Eloor 5866,7333,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median PM10 in the Monsoon season of 2018?,Thiruvananthapuram 5867,7335,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state noted the peak 75th percentile of PM2.5 in the Monsoon season of 2019?,Uttarakhand 5868,7336,spatio_temporal_aggregation,Which station had the highest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the peak median PM10 in the Post-Monsoon season of 2019?,"Zero Point GICI, Gangtok - SSPCB" 5869,7337,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state registered the minimum average PM10 in the Post-Monsoon season of 2019?,Kerala 5870,7339,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station registered the 2nd most minimal median PM10 throughout the Monsoon season of 2020.,"Lumpyngngad, Shillong - Meghalaya PCB" 5871,7340,spatio_temporal_aggregation,Which state had the highest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that showed the highest average PM10 over the Winter season of 2019.,Uttarakhand 5872,7341,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station possessed the 3rd lowest 75th percentile for PM10 in the Summer season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5873,7342,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station exhibiting the peak median PM2.5 during the Summer season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 5874,7343,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city experienced the third most minimal 75th percentile of PM2.5 throughout the Post-Monsoon season of 2019.,Thiruvananthapuram 5875,7345,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the 3rd lowest 25th percentile of PM10 in the Winter season of 2024?,"Airport Area, Indore - IMC" 5876,7347,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 75th percentile for PM10 throughout the Winter season of 2018.,Kerala 5877,7349,spatio_temporal_aggregation,Which station had the 2nd highest average PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest average PM10 in the Summer season of 2024?,"Vijay Nagar Scheme-78, Indore - Glenmark" 5878,7350,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that showed the second highest median PM2.5 during the Summer season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 5879,7351,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state registered the 2nd most minimal 75th percentile of PM2.5 throughout the Post-Monsoon season of 2022.,Sikkim 5880,7352,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station that experienced the 3rd lowest average PM2.5 over the Monsoon season of 2024.,"DM College of Science, Imphal - Manipur PCB" 5881,7353,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state possessed the 2nd highest 75th percentile for PM2.5 in the Summer season of 2024?,Tripura 5882,7354,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station exhibiting the second lowest average PM2.5 during the Post-Monsoon season of 2020.,"Udyogamandal, Eloor - Kerala PCB" 5883,7356,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that showed the 3rd highest 75th percentile of PM10 over the Monsoon season of 2022.,Vijayawada 5884,7357,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest 25th percentile for PM2.5 in the Summer season of 2022?,Aizawl 5885,7358,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 75th percentile of PM2.5 during the Monsoon season of 2019.,Sikkim 5886,7359,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city exhibited the peak 25th percentile of PM2.5 throughout the Post-Monsoon season of 2022.,Virudhunagar 5887,7360,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that recorded the second most minimal average PM2.5 over the Monsoon season of 2018.,"Chikkaballapur Rural, Chikkaballapur - KSPCB" 5888,7361,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest 25th percentile for PM10 in the Monsoon season of 2018?,"Yerramukkapalli, Kadapa - APPCB" 5889,7362,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state possessing the third lowest 75th percentile of PM10 during the Post-Monsoon season of 2020.,Tamil Nadu 5890,7363,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal median PM2.5 throughout the Post-Monsoon season of 2019.,Meghalaya 5891,7364,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that registered the second highest 75th percentile of PM2.5 over the Winter season of 2024.,Himachal Pradesh 5892,7366,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that showed the peak average PM2.5 during the Post-Monsoon season of 2022.,Virudhunagar 5893,7367,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the second highest 75th percentile of PM10 throughout the Post-Monsoon season of 2019.,"Yerramukkapalli, Kadapa - APPCB" 5894,7368,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd most minimal average PM2.5 over the Summer season of 2024.,"IESD Banaras Hindu University, Varanasi - UPPCB" 5895,7369,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city experienced the 3rd lowest average for PM2.5 in the Summer season of 2020?,Mysuru 5896,7370,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest 25th percentile of PM10 during the Summer season of 2023.,Bihar 5897,7371,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest 75th percentile for PM10 throughout the Winter season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 5898,7372,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that showed the second highest 75th percentile of PM2.5 over the Monsoon season of 2022.,Delhi 5899,7373,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest median for PM2.5 in the Winter season of 2018?,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 5900,7374,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city exhibiting the third highest 25th percentile of PM2.5 during the Summer season of 2024.,Thoothukudi 5901,7376,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest median for PM2.5 over the Winter season of 2021.,Vijayawada 5902,7377,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 75th percentile of PM10 in the Monsoon season of 2023?,Delhi 5903,7379,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the most minimal 25th percentile of PM2.5 throughout the Summer season of 2024.,"IESD Banaras Hindu University, Varanasi - UPPCB" 5904,7380,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine which station exhibited the 2nd lowest 75th percentile of PM10 over the Post-Monsoon season of 2020.,"Lumpyngngad, Shillong - Meghalaya PCB" 5905,7381,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest median for PM2.5 in the Winter season of 2021?,Shillong 5906,7383,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2019.,Virar 5907,7384,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM2.5 over the Monsoon season of 2024.,"Vijay Nagar Scheme-78, Indore - Glenmark" 5908,7387,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city registered the 2nd most minimal median for PM2.5 throughout the Summer season of 2022.,Gangtok 5909,7388,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state that showed the lowest 75th percentile of PM10 over the Monsoon season of 2019.,Meghalaya 5910,7391,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state experienced the peak 25th percentile of PM2.5 throughout the Monsoon season of 2018.,Uttarakhand 5911,7393,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest average PM10 in the Winter season of 2019?,Tripura 5912,7394,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that registered the peak 25th percentile for PM10 during the Post-Monsoon season of 2024.,Vapi 5913,7395,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third most minimal 75th percentile of PM10 throughout the Summer season of 2018.,Thiruvananthapuram 5914,7397,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average for PM10 in the Post-Monsoon season of 2020?,"Vikas Sadan, Gurugram - HSPCB" 5915,7398,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that showed the second lowest median PM2.5 during the Post-Monsoon season of 2021.,Aizawl 5916,7399,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest average PM2.5 throughout the Post-Monsoon season of 2021.,Manipur 5917,7400,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal 25th percentile of PM10 over the Post-Monsoon season of 2019.,Kolar 5918,7401,spatio_temporal_aggregation,Which city had the highest average PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest average for PM10 in the Summer season of 2024?,Tirunelveli 5919,7402,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the second lowest median PM10 during the Summer season of 2019.,"Manali Village, Chennai - TNPCB" 5920,7403,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city registered the 2nd most minimal average for PM10 throughout the Monsoon season of 2019.,Eloor 5921,7405,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station possessed the highest 25th percentile for PM2.5 in the Summer season of 2021?,"Zero Point GICI, Gangtok - SSPCB" 5922,7407,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal 25th percentile of PM2.5 throughout the Winter season of 2019.,Eloor 5923,7408,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city that recorded the lowest average for PM2.5 over the Post-Monsoon season of 2022.,Aizawl 5924,7409,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest median PM10 in the Post-Monsoon season of 2020?,Sikkim 5925,7410,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that registered the second lowest 25th percentile for PM10 during the Summer season of 2022.,"Kompally Municipal Office, Hyderabad - TSPCB" 5926,7411,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak 25th percentile of PM2.5 throughout the Post-Monsoon season of 2024.,Thoothukudi 5927,7412,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 75th percentile of PM10 over the Winter season of 2024.,Puducherry 5928,7413,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city recorded the 2nd highest average for PM10 in the Winter season of 2023?,Rohtak 5929,7414,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest median PM2.5 during the Winter season of 2021.,Mizoram 5930,7415,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest median PM2.5 throughout the Summer season of 2021.,"Vijay Nagar, Sangli - MPCB" 5931,7416,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 75th percentile of PM10 over the Winter season of 2023.,Bihar 5932,7417,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state experienced the highest 25th percentile for PM2.5 in the Winter season of 2018?,Uttarakhand 5933,7418,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the peak 75th percentile of PM10 during the Summer season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 5934,7420,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the peak median PM2.5 over the Monsoon season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 5935,7421,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station possessed the lowest average for PM2.5 in the Monsoon season of 2024?,"Sikulpuikawn, Aizawl - Mizoram PCB" 5936,7423,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station experienced the 2nd highest 75th percentile of PM2.5 throughout the Winter season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 5937,7424,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest median for PM10 over the Monsoon season of 2018.,Vrindavan 5938,7425,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest 25th percentile of PM10 in the Post-Monsoon season of 2024?,Gangtok 5939,7426,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 25th percentile for PM2.5 during the Winter season of 2018.,Sikkim 5940,7427,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal 25th percentile of PM10 throughout the Summer season of 2021.,Puducherry 5941,7428,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine which station exhibited the highest 25th percentile of PM2.5 over the Winter season of 2019.,"Zero Point GICI, Gangtok - SSPCB" 5942,7429,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 75th percentile for PM10 in the Post-Monsoon season of 2023?,Haryana 5943,7430,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that showed the third highest 25th percentile of PM10 during the Winter season of 2018.,"Worli, Mumbai - MPCB" 5944,7432,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest 75th percentile of PM2.5 over the Winter season of 2022.,"Sikulpuikawn, Aizawl - Mizoram PCB" 5945,7433,spatio_temporal_aggregation,Which state had the highest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state experienced the highest average for PM10 in the Monsoon season of 2024?,Delhi 5946,7435,spatio_temporal_aggregation,Which station had the highest median PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station registered the peak median for PM10 throughout the Monsoon season of 2024.,"Vyttila, Kochi - Kerala PCB" 5947,7436,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd most minimal average PM2.5 over the Post-Monsoon season of 2024.,"Mahatma Basaveswar Colony, Kalaburgi - KSPCB" 5948,7437,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state possessed the highest 25th percentile for PM2.5 in the Summer season of 2019?,Uttarakhand 5949,7438,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state exhibiting the second highest 75th percentile of PM10 during the Post-Monsoon season of 2022.,Bihar 5950,7439,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state experienced the 2nd highest median PM2.5 throughout the Monsoon season of 2020.,Tripura 5951,7440,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city that recorded the most minimal average for PM10 over the Summer season of 2019.,Eloor 5952,7441,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest average PM10 in the Monsoon season of 2021?,Shillong 5953,7442,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that registered the third lowest 75th percentile for PM2.5 during the Monsoon season of 2024.,"Kumaran College, Tirupur - TNPCB" 5954,7443,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 75th percentile of PM2.5 throughout the Post-Monsoon season of 2018.,"Worli, Mumbai - MPCB" 5955,7444,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the lowest 25th percentile of PM10 over the Post-Monsoon season of 2023.,Sikkim 5956,7445,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest 75th percentile for PM10 in the Winter season of 2023?,"Velippalayam, Nagapattinam - TNPCB" 5957,7446,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that showed the third lowest median PM10 during the Summer season of 2024.,Sikkim 5958,7447,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 75th percentile of PM10 throughout the Winter season of 2018.,Vijayawada 5959,7450,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the peak 25th percentile of PM10 during the Monsoon season of 2024.,Himachal Pradesh 5960,7451,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state registered the third highest 25th percentile for PM2.5 throughout the Summer season of 2018.,Sikkim 5961,7453,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city possessed the 3rd lowest 25th percentile for PM2.5 in the Summer season of 2024?,Surat 5962,7454,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state exhibiting the peak median PM2.5 during the Winter season of 2018.,Uttarakhand 5963,7455,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state experienced the most minimal median PM2.5 throughout the Post-Monsoon season of 2022.,Mizoram 5964,7456,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state that recorded the 3rd lowest 25th percentile of PM10 over the Monsoon season of 2023.,Arunachal Pradesh 5965,7457,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest average PM2.5 in the Summer season of 2019?,"Yerramukkapalli, Kadapa - APPCB" 5966,7458,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest 25th percentile for PM10 during the Post-Monsoon season of 2019.,Virudhunagar 5967,7459,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest median PM2.5 throughout the Monsoon season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 5968,7460,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal median PM10 over the Winter season of 2019.,Tamil Nadu 5969,7462,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that showed the second lowest average PM10 during the Winter season of 2019.,"Urban, Chamarajanagar - KSPCB" 5970,7464,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 25th percentile of PM10 over the Post-Monsoon season of 2024.,"Sahilara, Maihar - KJS Cements" 5971,7465,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Which state experienced the 3rd lowest median for PM10 in the Post-Monsoon season of 2024?,Manipur 5972,7466,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that recorded the peak 25th percentile of PM10 during the Post-Monsoon season of 2024.,Delhi 5973,7468,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the highest 25th percentile of PM10 over the Winter season of 2018.,"Zero Point GICI, Gangtok - SSPCB" 5974,7469,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state possessed the 3rd highest median for PM10 in the Summer season of 2021?,Manipur 5975,7470,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak median PM2.5 during the Summer season of 2022.,Virudhunagar 5976,7473,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest average PM10 in the Post-Monsoon season of 2023?,"GIDC, Nandesari - Nandesari Ind. Association" 5977,7476,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest average PM2.5 over the Monsoon season of 2024.,Punjab 5978,7478,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that showed the second highest 75th percentile of PM2.5 during the Monsoon season of 2021.,Sikkim 5979,7480,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest 25th percentile of PM10 over the Winter season of 2022.,"Vikas Sadan, Gurugram - HSPCB" 5980,7481,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city experienced the lowest 25th percentile for PM10 in the Post-Monsoon season of 2018?,Talcher 5981,7482,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the peak 25th percentile of PM2.5 during the Monsoon season of 2024.,Thoothukudi 5982,7483,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the 2nd highest median for PM10 throughout the Post-Monsoon season of 2021.,Virar 5983,7485,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest 25th percentile for PM10 in the Summer season of 2024?,"Crescent University, Chengalpattu - TNPCB" 5984,7487,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state experienced the most minimal median PM10 throughout the Post-Monsoon season of 2023.,Sikkim 5985,7488,spatio_temporal_aggregation,Which station had the highest average PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that recorded the highest average for PM10 over the Monsoon season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 5986,7489,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city showed the 3rd lowest 25th percentile of PM10 in the Summer season of 2024?,Surat 5987,7490,spatio_temporal_aggregation,Which station had the highest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the peak average for PM10 during the Monsoon season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 5988,7491,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the highest 75th percentile of PM10 throughout the Post-Monsoon season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 5989,7493,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile for PM2.5 in the Monsoon season of 2019?,Yadgir 5990,7494,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that showed the third lowest median PM10 during the Post-Monsoon season of 2022.,"Brahmagiri, Udupi - KSPCB" 5991,7496,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest 25th percentile of PM10 over the Post-Monsoon season of 2022.,"Mazgaon, Mumbai - IITM" 5992,7497,spatio_temporal_aggregation,Which city had the highest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest median for PM10 in the Summer season of 2019?,Yadgir 5993,7499,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city registered the 3rd most minimal median for PM10 throughout the Post-Monsoon season of 2020.,Kannur 5994,7500,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that showed the second highest 75th percentile of PM10 over the Monsoon season of 2022.,Himachal Pradesh 5995,7501,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city possessed the 3rd highest average for PM10 in the Monsoon season of 2021?,Vijayawada 5996,7503,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city experienced the third lowest average PM10 throughout the Post-Monsoon season of 2024.,Koppal 5997,7504,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that recorded the 2nd highest average for PM10 over the Summer season of 2019.,Vrindavan 5998,7505,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state showed the 3rd lowest 75th percentile of PM2.5 in the Monsoon season of 2024?,Manipur 5999,7506,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third lowest 25th percentile for PM2.5 during the Monsoon season of 2022.,Manipur 6000,7507,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 75th percentile of PM10 throughout the Monsoon season of 2018.,Siliguri 6001,7508,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest 25th percentile of PM2.5 over the Summer season of 2024.,Chengalpattu 6002,7509,spatio_temporal_aggregation,Which state had the highest average PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average for PM10 in the Winter season of 2021?,Uttarakhand 6003,7510,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest 25th percentile of PM2.5 during the Monsoon season of 2024.,Manipur 6004,7511,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest average PM2.5 throughout the Monsoon season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6005,7512,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal median PM10 over the Post-Monsoon season of 2021.,Mizoram 6006,7513,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state experienced the highest 25th percentile for PM2.5 in the Post-Monsoon season of 2022?,Jharkhand 6007,7514,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the second highest median PM2.5 during the Winter season of 2020.,"Yerramukkapalli, Kadapa - APPCB" 6008,7515,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the 2nd highest 25th percentile for PM2.5 throughout the Winter season of 2023.,Thoothukudi 6009,7517,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state possessed the most minimal median for PM2.5 in the Winter season of 2021?,Meghalaya 6010,7518,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city exhibiting the lowest 25th percentile of PM10 during the Monsoon season of 2019.,Bathinda 6011,7519,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal average PM10 throughout the Monsoon season of 2020.,Shillong 6012,7521,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest median PM10 in the Monsoon season of 2021?,"Yerramukkapalli, Kadapa - APPCB" 6013,7522,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third lowest average for PM2.5 during the Monsoon season of 2022.,Manipur 6014,7523,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 75th percentile of PM2.5 throughout the Winter season of 2024.,Sikkim 6015,7524,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd most minimal average PM10 over the Post-Monsoon season of 2023.,Mizoram 6016,7525,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 25th percentile for PM10 in the Monsoon season of 2021?,"Sikulpuikawn, Aizawl - Mizoram PCB" 6017,7526,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that showed the third lowest 75th percentile of PM10 during the Post-Monsoon season of 2018.,Karnataka 6018,7528,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest median PM10 over the Post-Monsoon season of 2023.,Mizoram 6019,7529,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state experienced the 3rd lowest average for PM2.5 in the Post-Monsoon season of 2023?,Nagaland 6020,7530,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest average PM2.5 during the Post-Monsoon season of 2024.,Manipur 6021,7531,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station registered the 3rd most minimal 25th percentile of PM2.5 throughout the Summer season of 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 6022,7532,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state that showed the lowest average PM2.5 over the Monsoon season of 2022.,Mizoram 6023,7534,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station exhibiting the most minimal 25th percentile of PM10 during the Post-Monsoon season of 2019.,"Lumpyngngad, Shillong - Meghalaya PCB" 6024,7535,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state experienced the third lowest median PM10 throughout the Summer season of 2018.,Karnataka 6025,7536,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that recorded the 3rd highest average for PM2.5 over the Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6026,7537,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest median PM10 in the Winter season of 2024?,Tirunelveli 6027,7538,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that registered the most minimal average for PM10 during the Monsoon season of 2018.,"Hombegowda Nagar, Bengaluru - KSPCB" 6028,7539,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third lowest 75th percentile of PM10 throughout the Winter season of 2021.,Vijayapura 6029,7540,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd most minimal 75th percentile of PM10 over the Summer season of 2024.,"Semmandalam, Cuddalore - TNPCB" 6030,7542,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that showed the second lowest 25th percentile of PM2.5 during the Summer season of 2021.,"Diwator Nagar, Koppal - KSPCB" 6031,7543,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third most minimal 75th percentile of PM10 throughout the Post-Monsoon season of 2018.,Chikkaballapur 6032,7544,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd lowest 75th percentile of PM2.5 over the Post-Monsoon season of 2018.,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 6033,7545,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state experienced the 2nd lowest average for PM10 in the Post-Monsoon season of 2024?,Meghalaya 6034,7549,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city possessed the 3rd lowest 75th percentile for PM2.5 in the Monsoon season of 2020?,Mysuru 6035,7551,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city experienced the peak 25th percentile of PM2.5 throughout the Post-Monsoon season of 2023.,Virudhunagar 6036,7554,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that registered the third lowest average for PM10 during the Monsoon season of 2019.,"Bidhannagar, Kolkata - WBPCB" 6037,7555,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6038,7556,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest median PM10 over the Monsoon season of 2022.,"Vijay Nagar, Sangli - MPCB" 6039,7557,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest 75th percentile for PM10 in the Winter season of 2024?,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6040,7558,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that showed the second highest 75th percentile of PM10 during the Post-Monsoon season of 2021.,Virar 6041,7559,spatio_temporal_aggregation,Which city had the highest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak average PM10 throughout the Post-Monsoon season of 2024.,Vapi 6042,7560,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest average PM10 over the Winter season of 2020.,Virar 6043,7561,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city experienced the 2nd highest 75th percentile for PM2.5 in the Post-Monsoon season of 2019?,Virudhunagar 6044,7563,spatio_temporal_aggregation,Which station had the highest average PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station registered the peak average for PM10 throughout the Monsoon season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 6045,7564,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal average PM2.5 over the Winter season of 2018.,Chandrapur 6046,7565,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest average for PM2.5 in the Summer season of 2021?,Vrindavan 6047,7566,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station exhibiting the most minimal median PM2.5 during the Post-Monsoon season of 2021.,"Kamalnath Nagar, Bettiah - BSPCB" 6048,7567,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city experienced the 2nd highest average PM10 throughout the Post-Monsoon season of 2018.,Yadgir 6049,7568,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that recorded the 2nd highest median for PM2.5 over the Post-Monsoon season of 2018.,Yadgir 6050,7569,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 25th percentile of PM10 in the Summer season of 2019?,Virudhunagar 6051,7570,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest average for PM10 during the Monsoon season of 2020.,Virudhunagar 6052,7571,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal median PM10 throughout the Monsoon season of 2020.,Meghalaya 6053,7573,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile for PM10 in the Post-Monsoon season of 2023?,Virudhunagar 6054,7575,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2024.,"Sector-116, Noida - UPPCB" 6055,7576,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM10 over the Winter season of 2022.,Virar 6056,7577,spatio_temporal_aggregation,Which station had the highest median PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station experienced the highest median for PM10 in the Summer season of 2021?,"Zero Point GICI, Gangtok - SSPCB" 6057,7578,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest 25th percentile of PM10 during the Summer season of 2020.,Tripura 6058,7579,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state registered the 2nd most minimal average for PM2.5 throughout the Monsoon season of 2018.,Telangana 6059,7581,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state possessed the 2nd lowest average for PM10 in the Post-Monsoon season of 2019?,Tamil Nadu 6060,7582,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station exhibiting the third most minimal median PM10 during the Monsoon season of 2018.,"Hebbal, Bengaluru - KSPCB" 6061,7583,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report which station experienced the third highest median PM10 throughout the Monsoon season of 2018.,"Worli, Mumbai - MPCB" 6062,7584,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest 25th percentile of PM2.5 over the Winter season of 2024.,Hosur 6063,7585,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest 75th percentile for PM2.5 in the Summer season of 2019?,Yadgir 6064,7586,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that registered the 2nd most minimal median PM10 during the Summer season of 2019.,Chennai 6065,7587,spatio_temporal_aggregation,Which city had the highest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak median PM10 throughout the Post-Monsoon season of 2022.,Virudhunagar 6066,7588,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest average PM10 over the Summer season of 2021.,Virar 6067,7589,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile for PM10 in the Monsoon season of 2020?,Uttarakhand 6068,7590,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that showed the most minimal 25th percentile of PM10 during the Monsoon season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6069,7591,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 25th percentile of PM2.5 throughout the Monsoon season of 2022.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6070,7592,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest median PM10 over the Summer season of 2024.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6071,7593,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station experienced the lowest 75th percentile for PM2.5 in the Post-Monsoon season of 2024?,"Sikulpuikawn, Aizawl - Mizoram PCB" 6072,7594,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the second highest median PM10 during the Summer season of 2023.,"Vijay Nagar, Sangli - MPCB" 6073,7595,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station registered the 3rd most minimal 75th percentile of PM2.5 throughout the Monsoon season of 2021.,"Diwator Nagar, Koppal - KSPCB" 6074,7597,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state possessed the highest median for PM2.5 in the Summer season of 2024?,Delhi 6075,7598,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state exhibiting the third lowest median PM10 during the Winter season of 2022.,Sikkim 6076,7599,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station experienced the 2nd highest 25th percentile of PM10 throughout the Post-Monsoon season of 2023.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6077,7601,spatio_temporal_aggregation,Which state had the highest median PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest median PM10 in the Monsoon season of 2019?,Uttarakhand 6078,7602,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the lowest 75th percentile for PM10 during the Post-Monsoon season of 2019.,Eloor 6079,7603,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest median PM10 throughout the Monsoon season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6080,7604,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 75th percentile of PM2.5 over the Post-Monsoon season of 2023.,Haryana 6081,7605,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest median for PM10 in the Summer season of 2022?,Gangtok 6082,7607,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest average PM2.5 throughout the Summer season of 2023.,Ulhasnagar 6083,7609,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city experienced the lowest 25th percentile for PM10 in the Winter season of 2022?,Nandesari 6084,7610,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the peak 75th percentile of PM2.5 during the Post-Monsoon season of 2018.,Yamuna Nagar 6085,7611,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state registered the third highest 75th percentile for PM2.5 throughout the Winter season of 2018.,Sikkim 6086,7612,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the highest 25th percentile of PM10 over the Post-Monsoon season of 2022.,"Yerramukkapalli, Kadapa - APPCB" 6087,7613,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest 75th percentile for PM10 in the Winter season of 2023?,Virudhunagar 6088,7614,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak average PM2.5 during the Post-Monsoon season of 2023.,Virudhunagar 6089,7615,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station experienced the 2nd most minimal 75th percentile of PM10 throughout the Summer season of 2021.,"Brahmagiri, Udupi - KSPCB" 6090,7616,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city that recorded the 2nd lowest 75th percentile of PM10 over the Summer season of 2024.,Chengalpattu 6091,7617,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city showed the 2nd lowest 75th percentile for PM10 in the Post-Monsoon season of 2018?,Vijayawada 6092,7619,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest 75th percentile of PM10 throughout the Monsoon season of 2019.,Virudhunagar 6093,7620,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest average PM2.5 over the Monsoon season of 2024.,"Velippalayam, Nagapattinam - TNPCB" 6094,7621,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest average for PM2.5 in the Post-Monsoon season of 2022?,Delhi 6095,7623,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest average PM2.5 throughout the Summer season of 2023.,Delhi 6096,7624,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest median PM10 over the Summer season of 2018.,Tamil Nadu 6097,7625,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city experienced the 2nd highest average for PM10 in the Post-Monsoon season of 2023?,Tiruchirappalli 6098,7626,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that recorded the third lowest 25th percentile of PM10 during the Winter season of 2023.,Gangtok 6099,7628,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd lowest average PM2.5 over the Summer season of 2021.,"Ratanpura, Rupnagar - Ambuja Cements" 6100,7629,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest 75th percentile for PM2.5 in the Summer season of 2021?,"Ratanpura, Rupnagar - Ambuja Cements" 6101,7631,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state experienced the peak median PM2.5 throughout the Monsoon season of 2019.,Uttarakhand 6102,7633,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest average PM2.5 in the Winter season of 2024?,"Zero Point GICI, Gangtok - SSPCB" 6103,7634,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest average for PM2.5 during the Post-Monsoon season of 2019.,Satna 6104,7635,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest 25th percentile of PM2.5 throughout the Winter season of 2018.,Tripura 6105,7637,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest 75th percentile for PM10 in the Winter season of 2019?,Coimbatore 6106,7638,spatio_temporal_aggregation,Which station had the 2nd highest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that showed the second highest average PM10 during the Winter season of 2024.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6107,7639,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third lowest median PM10 throughout the Winter season of 2019.,Kerala 6108,7640,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal median PM10 over the Winter season of 2023.,Mizoram 6109,7642,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the most minimal average PM2.5 during the Summer season of 2023.,Silchar 6110,7644,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd lowest average PM2.5 over the Monsoon season of 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6111,7645,spatio_temporal_aggregation,Which city had the highest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest average for PM10 in the Monsoon season of 2018?,Yamuna Nagar 6112,7646,spatio_temporal_aggregation,Which state had the highest median PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state exhibiting the peak median PM10 during the Post-Monsoon season of 2024.,Delhi 6113,7647,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city experienced the third lowest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2018.,Tirupati 6114,7648,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city that recorded the 2nd most minimal 25th percentile of PM10 over the Monsoon season of 2022.,Gangtok 6115,7650,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third lowest median for PM10 during the Post-Monsoon season of 2018.,Karnataka 6116,7651,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state possessed the peak average PM2.5 throughout the Post-Monsoon season of 2019.,Uttarakhand 6117,7652,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal 25th percentile of PM10 over the Winter season of 2019.,Tamil Nadu 6118,7656,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM2.5 over the Summer season of 2023.,Virar 6119,7658,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third highest 75th percentile of PM10 during the Post-Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6120,7660,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station that showed the 3rd lowest 75th percentile of PM2.5 over the Post-Monsoon season of 2022.,"PSG College of Arts and Science, Coimbatore - TNPCB" 6121,7661,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest median for PM2.5 in the Winter season of 2022?,"Sikulpuikawn, Aizawl - Mizoram PCB" 6122,7663,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city experienced the 2nd highest average PM2.5 throughout the Summer season of 2021.,Virudhunagar 6123,7664,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city that recorded the 3rd lowest average PM10 over the Monsoon season of 2024.,Shillong 6124,7665,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 25th percentile for PM10 in the Winter season of 2019?,Virar 6125,7666,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that registered the third lowest average for PM2.5 during the Post-Monsoon season of 2024.,"DM College of Science, Imphal - Manipur PCB" 6126,7667,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest average PM2.5 throughout the Winter season of 2022.,Mizoram 6127,7668,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the most minimal median PM2.5 over the Post-Monsoon season of 2019.,Eloor 6128,7670,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that showed the third highest average PM10 during the Winter season of 2024.,"Sector-6, Panchkula - HSPCB" 6129,7671,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest 25th percentile of PM10 throughout the Monsoon season of 2018.,Yadgir 6130,7674,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the third lowest average PM2.5 during the Winter season of 2021.,Chhattisgarh 6131,7675,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state registered the 2nd highest 25th percentile for PM2.5 throughout the Winter season of 2023.,Bihar 6132,7676,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal 25th percentile of PM2.5 over the Winter season of 2023.,Gangtok 6133,7677,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state possessed the 3rd highest median for PM10 in the Monsoon season of 2019?,Sikkim 6134,7678,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city exhibiting the 2nd lowest 75th percentile of PM10 during the Winter season of 2019.,Chamarajanagar 6135,7679,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report which station experienced the most minimal median PM10 throughout the Monsoon season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6136,7680,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station that recorded the 3rd lowest median PM10 over the Winter season of 2024.,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 6137,7681,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile for PM2.5 in the Monsoon season of 2018?,Yadgir 6138,7682,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the most minimal median PM2.5 during the Winter season of 2020.,Aizawl 6139,7683,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd lowest 25th percentile of PM2.5 throughout the Monsoon season of 2021.,Aizawl 6140,7684,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 75th percentile of PM10 over the Post-Monsoon season of 2018.,Tripura 6141,7685,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median for PM2.5 in the Post-Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 6142,7686,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest 25th percentile of PM10 during the Post-Monsoon season of 2021.,Mizoram 6143,7687,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest 25th percentile of PM2.5 throughout the Monsoon season of 2018.,Sikkim 6144,7688,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal 25th percentile of PM2.5 over the Post-Monsoon season of 2020.,"Sanathnagar, Hyderabad - TSPCB" 6145,7689,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station experienced the 2nd lowest average for PM2.5 in the Summer season of 2020?,"Manali Village, Chennai - TNPCB" 6146,7690,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the lowest 25th percentile of PM2.5 during the Monsoon season of 2018.,Kerala 6147,7692,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that showed the 3rd highest average PM2.5 over the Winter season of 2024.,Tripura 6148,7693,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest 25th percentile for PM2.5 in the Post-Monsoon season of 2024?,"Kumaran College, Tirupur - TNPCB" 6149,7694,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state exhibiting the 2nd most minimal 25th percentile of PM10 during the Post-Monsoon season of 2018.,Kerala 6150,7698,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 25th percentile for PM10 during the Winter season of 2021.,Manipur 6151,7699,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest 25th percentile of PM10 throughout the Post-Monsoon season of 2023.,Himachal Pradesh 6152,7700,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal 25th percentile of PM10 over the Monsoon season of 2021.,Udupi 6153,7701,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile for PM10 in the Post-Monsoon season of 2023?,Delhi 6154,7702,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that showed the third lowest average PM2.5 during the Post-Monsoon season of 2024.,Gangtok 6155,7703,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the peak median PM2.5 throughout the Monsoon season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 6156,7704,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd most minimal average PM2.5 over the Monsoon season of 2020.,Maharashtra 6157,7705,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station experienced the 3rd highest 25th percentile for PM10 in the Post-Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 6158,7706,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third highest 25th percentile of PM10 during the Post-Monsoon season of 2021.,"Vikas Sadan, Gurugram - HSPCB" 6159,7707,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station registered the most minimal 25th percentile of PM2.5 throughout the Winter season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6160,7708,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that showed the 3rd highest 25th percentile of PM2.5 over the Monsoon season of 2021.,Vijayawada 6161,7709,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest 25th percentile for PM10 in the Winter season of 2018?,Bengaluru 6162,7710,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city exhibiting the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023.,Tiruchirappalli 6163,7712,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state that recorded the lowest 75th percentile of PM2.5 over the Winter season of 2020.,Mizoram 6164,7713,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile for PM2.5 in the Summer season of 2020?,Virudhunagar 6165,7714,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the peak 25th percentile of PM10 during the Winter season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 6166,7717,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 25th percentile for PM10 in the Post-Monsoon season of 2024?,Pathardih 6167,7720,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the most minimal average PM2.5 over the Monsoon season of 2018.,Kerala 6168,7722,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the third lowest median PM10 during the Summer season of 2023.,Puducherry 6169,7724,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that showed the 3rd highest 75th percentile of PM2.5 over the Summer season of 2022.,Vijayawada 6170,7725,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station possessed the 3rd highest 75th percentile for PM10 in the Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 6171,7726,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state exhibiting the third highest 25th percentile of PM10 during the Summer season of 2022.,Haryana 6172,7727,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal median PM2.5 throughout the Monsoon season of 2018.,Tirupati 6173,7728,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city that recorded the lowest average for PM2.5 over the Post-Monsoon season of 2018.,Satna 6174,7729,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest median PM10 in the Winter season of 2024?,"Municipal Corporation Office, Tirunelveli - TNPCB" 6175,7730,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 25th percentile for PM2.5 during the Summer season of 2019.,Mumbai 6176,7731,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state possessed the peak average PM2.5 throughout the Summer season of 2018.,Uttarakhand 6177,7732,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal median PM10 over the Summer season of 2018.,Thiruvananthapuram 6178,7733,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest median for PM2.5 in the Winter season of 2020?,Mizoram 6179,7735,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak average PM2.5 throughout the Monsoon season of 2023.,Virudhunagar 6180,7736,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd lowest 75th percentile of PM10 over the Post-Monsoon season of 2024.,Tirunelveli 6181,7738,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest 75th percentile of PM2.5 during the Summer season of 2023.,Tripura 6182,7739,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station registered the 3rd most minimal 25th percentile of PM2.5 throughout the Winter season of 2023.,"Deen Dayal Nagar, Sagar - MPPCB" 6183,7740,spatio_temporal_aggregation,Which city had the highest median PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city that showed the peak median PM10 over the Monsoon season of 2024.,Thanjavur 6184,7742,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station exhibiting the peak 25th percentile of PM10 during the Monsoon season of 2018.,"Zero Point GICI, Gangtok - SSPCB" 6185,7743,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal 75th percentile of PM10 throughout the Monsoon season of 2020.,Meghalaya 6186,7744,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the 2nd highest 75th percentile of PM2.5 over the Post-Monsoon season of 2022.,"Vijay Nagar, Sangli - MPCB" 6187,7745,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest 75th percentile for PM10 in the Monsoon season of 2020?,"Vikas Sadan, Gurugram - HSPCB" 6188,7746,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 75th percentile for PM2.5 during the Summer season of 2019.,Tirupati 6189,7747,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest 25th percentile of PM10 throughout the Post-Monsoon season of 2019.,Sikkim 6190,7748,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd most minimal median PM10 over the Winter season of 2018.,Vijayawada 6191,7750,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that showed the lowest 75th percentile of PM10 during the Post-Monsoon season of 2022.,Udupi 6192,7751,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 25th percentile of PM10 throughout the Winter season of 2022.,Meghalaya 6193,7752,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest 75th percentile of PM10 over the Monsoon season of 2019.,Uttarakhand 6194,7753,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state experienced the 2nd highest 25th percentile for PM2.5 in the Winter season of 2024?,Delhi 6195,7754,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the most minimal average PM2.5 during the Post-Monsoon season of 2019.,"Udyogamandal, Eloor - Kerala PCB" 6196,7755,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report which station registered the most minimal 25th percentile of PM10 throughout the Monsoon season of 2018.,"Tamaka Ind. Area, Kolar - KSPCB" 6197,7756,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city that showed the peak median PM2.5 over the Monsoon season of 2024.,Thoothukudi 6198,7757,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station possessed the lowest 75th percentile for PM2.5 in the Monsoon season of 2024?,"Sikulpuikawn, Aizawl - Mizoram PCB" 6199,7758,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city exhibiting the most minimal 75th percentile of PM2.5 during the Monsoon season of 2024.,Aizawl 6200,7760,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state that recorded the 3rd lowest 75th percentile of PM2.5 over the Summer season of 2022.,Jammu and Kashmir 6201,7761,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city showed the 2nd lowest median for PM10 in the Summer season of 2024?,Surat 6202,7762,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that registered the second highest average PM10 during the Monsoon season of 2024.,Himachal Pradesh 6203,7763,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest 25th percentile of PM2.5 throughout the Winter season of 2022.,Virar 6204,7764,spatio_temporal_aggregation,Which state had the highest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the peak median PM10 over the Post-Monsoon season of 2023.,Delhi 6205,7765,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest 75th percentile for PM2.5 in the Post-Monsoon season of 2018?,Andhra Pradesh 6206,7768,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest average PM2.5 over the Summer season of 2018.,"Worli, Mumbai - MPCB" 6207,7769,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city experienced the lowest 75th percentile for PM10 in the Summer season of 2020?,Coimbatore 6208,7771,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state registered the most minimal average PM2.5 throughout the Post-Monsoon season of 2018.,Kerala 6209,7773,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state possessed the 3rd lowest 75th percentile for PM2.5 in the Winter season of 2024?,Puducherry 6210,7775,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city experienced the 2nd highest median PM10 throughout the Monsoon season of 2021.,Virar 6211,7776,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the 2nd most minimal median PM10 over the Post-Monsoon season of 2024.,Meghalaya 6212,7777,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the 2nd highest 25th percentile for PM2.5 in the Summer season of 2023?,Virar 6213,7778,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest median PM2.5 during the Summer season of 2018.,Sikkim 6214,7779,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak median PM2.5 throughout the Monsoon season of 2022.,Virudhunagar 6215,7780,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest average PM10 over the Winter season of 2021.,Sikkim 6216,7782,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that showed the most minimal median PM2.5 during the Monsoon season of 2020.,Mizoram 6217,7783,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 25th percentile of PM10 throughout the Summer season of 2022.,"Vijay Nagar, Sangli - MPCB" 6218,7784,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest median PM10 over the Post-Monsoon season of 2021.,Sikkim 6219,7785,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest 25th percentile for PM10 in the Summer season of 2022?,Virudhunagar 6220,7786,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest 75th percentile of PM10 during the Winter season of 2019.,Tripura 6221,7788,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal 25th percentile of PM10 over the Monsoon season of 2019.,Eloor 6222,7789,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state possessed the 2nd lowest median for PM10 in the Summer season of 2023?,Arunachal Pradesh 6223,7790,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state exhibiting the third lowest average PM2.5 during the Winter season of 2018.,Kerala 6224,7792,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that recorded the 3rd highest average PM10 over the Post-Monsoon season of 2018.,Tamil Nadu 6225,7793,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest median PM10 in the Post-Monsoon season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 6226,7794,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest median for PM10 during the Post-Monsoon season of 2023.,Himachal Pradesh 6227,7795,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal median PM2.5 throughout the Winter season of 2019.,Kerala 6228,7796,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest 75th percentile of PM2.5 over the Summer season of 2022.,Sikkim 6229,7797,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median for PM2.5 in the Winter season of 2021?,Bagalkot 6230,7798,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that showed the second highest 25th percentile of PM10 during the Summer season of 2020.,"Yerramukkapalli, Kadapa - APPCB" 6231,7800,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest average PM2.5 over the Post-Monsoon season of 2018.,"Zero Point GICI, Gangtok - SSPCB" 6232,7801,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest 25th percentile for PM10 in the Winter season of 2022?,Virudhunagar 6233,7802,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the most minimal median PM2.5 during the Winter season of 2018.,Karnataka 6234,7803,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state registered the 3rd most minimal 25th percentile of PM10 throughout the Summer season of 2020.,Tamil Nadu 6235,7804,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd lowest average PM2.5 over the Winter season of 2020.,"Udyogamandal, Eloor - Kerala PCB" 6236,7806,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station exhibiting the peak 75th percentile of PM2.5 during the Summer season of 2018.,"Zero Point GICI, Gangtok - SSPCB" 6237,7807,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state experienced the third lowest 25th percentile of PM2.5 throughout the Monsoon season of 2019.,Jharkhand 6238,7809,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest 25th percentile for PM10 in the Summer season of 2018?,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 6239,7810,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second lowest 75th percentile of PM2.5 during the Summer season of 2024.,Puducherry 6240,7811,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third most minimal average PM2.5 throughout the Summer season of 2018.,Amaravati 6241,7812,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest median PM10 over the Winter season of 2023.,Rohtak 6242,7813,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 25th percentile for PM2.5 in the Post-Monsoon season of 2024?,"DM College of Science, Imphal - Manipur PCB" 6243,7815,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest average PM2.5 throughout the Post-Monsoon season of 2018.,Sikkim 6244,7817,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state experienced the highest average for PM2.5 in the Post-Monsoon season of 2024?,Delhi 6245,7818,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the second highest 75th percentile of PM10 during the Monsoon season of 2024.,"Vikas Sadan, Gurugram - HSPCB" 6246,7819,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the 2nd highest median for PM2.5 throughout the Winter season of 2019.,Virudhunagar 6247,7820,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal 75th percentile of PM10 over the Monsoon season of 2023.,Koppal 6248,7821,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city possessed the 2nd highest average for PM10 in the Monsoon season of 2019?,Vrindavan 6249,7822,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city exhibiting the third highest 75th percentile of PM10 during the Summer season of 2018.,Vrindavan 6250,7823,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal average PM2.5 throughout the Summer season of 2018.,Rajamahendravaram 6251,7824,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the 2nd highest average PM2.5 over the Post-Monsoon season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6252,7825,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest 75th percentile for PM10 in the Post-Monsoon season of 2018?,"PWD Grounds, Vijayawada - APPCB" 6253,7826,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 75th percentile of PM2.5 during the Post-Monsoon season of 2020.,Puducherry 6254,7827,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report which city possessed the lowest 25th percentile of PM10 throughout the Winter season of 2019.,Shillong 6255,7828,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the most minimal average PM2.5 over the Winter season of 2023.,Aizawl 6256,7830,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that showed the third lowest average PM2.5 during the Summer season of 2020.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 6257,7831,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest 75th percentile of PM2.5 throughout the Winter season of 2019.,Tripura 6258,7832,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM10 over the Summer season of 2023.,Virar 6259,7833,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city experienced the 3rd highest 75th percentile for PM2.5 in the Summer season of 2020?,Virar 6260,7834,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the peak average PM2.5 during the Post-Monsoon season of 2022.,"Yerramukkapalli, Kadapa - APPCB" 6261,7835,spatio_temporal_aggregation,Which station had the highest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station registered the peak average for PM10 throughout the Summer season of 2019.,"Zero Point GICI, Gangtok - SSPCB" 6262,7836,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state that showed the 2nd lowest 75th percentile of PM2.5 over the Winter season of 2018.,Maharashtra 6263,7837,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state possessed the 3rd highest average for PM10 in the Winter season of 2024?,Himachal Pradesh 6264,7838,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak median PM2.5 during the Summer season of 2018.,Yamuna Nagar 6265,7839,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state experienced the most minimal median PM10 throughout the Monsoon season of 2024.,Sikkim 6266,7841,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest 25th percentile for PM2.5 in the Summer season of 2022?,"Vijay Nagar Scheme-78, Indore - Glenmark" 6267,7842,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest median PM2.5 during the Winter season of 2020.,Puducherry 6268,7843,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 25th percentile of PM2.5 throughout the Post-Monsoon season of 2020.,Eloor 6269,7844,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd lowest average PM10 over the Post-Monsoon season of 2021.,Mizoram 6270,7846,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that showed the third lowest median PM2.5 during the Winter season of 2024.,Tirunelveli 6271,7847,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest 25th percentile of PM2.5 throughout the Winter season of 2019.,Tripura 6272,7848,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the highest 75th percentile of PM10 over the Post-Monsoon season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 6273,7849,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state experienced the 2nd highest 25th percentile for PM10 in the Winter season of 2023?,Bihar 6274,7850,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that recorded the third lowest 75th percentile of PM10 during the Monsoon season of 2019.,Jharkhand 6275,7851,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report which city registered the most minimal average for PM10 throughout the Summer season of 2022.,Udupi 6276,7853,spatio_temporal_aggregation,Which city had the highest average PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest average for PM10 in the Summer season of 2023?,Virudhunagar 6277,7854,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station exhibiting the third highest 75th percentile of PM2.5 during the Post-Monsoon season of 2022.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6278,7855,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station experienced the peak 25th percentile of PM10 throughout the Monsoon season of 2024.,"Vyttila, Kochi - Kerala PCB" 6279,7856,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city that recorded the 3rd lowest 75th percentile of PM2.5 over the Post-Monsoon season of 2023.,Aizawl 6280,7857,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 25th percentile for PM2.5 in the Post-Monsoon season of 2018?,Vrindavan 6281,7858,spatio_temporal_aggregation,Which state had the highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the peak median PM10 during the Post-Monsoon season of 2021.,Uttarakhand 6282,7859,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report which station possessed the 2nd most minimal median PM2.5 throughout the Monsoon season of 2020.,"Borivali East, Mumbai - MPCB" 6283,7860,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest average PM2.5 over the Summer season of 2024.,"Civil Lines, Bareilly - UPPCB" 6284,7861,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest median for PM10 in the Post-Monsoon season of 2022?,Udupi 6285,7862,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest 25th percentile of PM2.5 during the Summer season of 2021.,Meghalaya 6286,7863,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest 75th percentile of PM10 throughout the Monsoon season of 2019.,Vrindavan 6287,7865,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station experienced the 3rd lowest average for PM10 in the Winter season of 2022?,"GIDC, Nandesari - Nandesari Ind. Association" 6288,7868,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city that showed the 3rd most minimal 25th percentile of PM2.5 over the Monsoon season of 2021.,Koppal 6289,7869,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest 25th percentile for PM2.5 in the Monsoon season of 2023?,Virudhunagar 6290,7870,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state exhibiting the second highest average PM2.5 during the Monsoon season of 2022.,Delhi 6291,7873,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest median for PM10 in the Monsoon season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 6292,7874,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the peak 75th percentile of PM2.5 during the Summer season of 2019.,Uttarakhand 6293,7875,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 25th percentile of PM2.5 throughout the Winter season of 2018.,Chandrapur 6294,7877,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest 25th percentile for PM10 in the Summer season of 2018?,"Zero Point GICI, Gangtok - SSPCB" 6295,7878,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that showed the third highest median PM10 during the Summer season of 2023.,"Velippalayam, Nagapattinam - TNPCB" 6296,7879,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third lowest 75th percentile of PM2.5 throughout the Post-Monsoon season of 2018.,Vijayawada 6297,7880,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM2.5 over the Summer season of 2019.,"Worli, Mumbai - MPCB" 6298,7881,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city experienced the 3rd lowest median for PM2.5 in the Winter season of 2018?,Khanna 6299,7882,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the third highest 75th percentile of PM2.5 during the Summer season of 2021.,Virar 6300,7883,spatio_temporal_aggregation,Which state had the highest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report which state registered the peak median for PM10 throughout the Monsoon season of 2020.,Uttarakhand 6301,7884,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station that showed the lowest average PM2.5 over the Post-Monsoon season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6302,7885,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station possessed the highest 25th percentile for PM2.5 in the Post-Monsoon season of 2024?,"Vyttila, Kochi - Kerala PCB" 6303,7886,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state exhibiting the third most minimal 25th percentile of PM2.5 during the Summer season of 2019.,Tamil Nadu 6304,7887,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report which station experienced the third lowest average PM10 throughout the Summer season of 2020.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 6305,7888,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that recorded the 3rd highest median PM2.5 over the Monsoon season of 2022.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6306,7889,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 75th percentile for PM10 in the Summer season of 2018?,Andhra Pradesh 6307,7890,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 25th percentile for PM2.5 during the Winter season of 2018.,Bengaluru 6308,7891,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest average PM2.5 throughout the Summer season of 2020.,"Yerramukkapalli, Kadapa - APPCB" 6309,7892,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM10 over the Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6310,7893,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average for PM10 in the Post-Monsoon season of 2021?,Shillong 6311,7894,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Identify the state that showed the third lowest 75th percentile of PM2.5 during the Summer season of 2019.,Karnataka 6312,7896,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest median PM2.5 over the Post-Monsoon season of 2024.,Manipur 6313,7898,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the second lowest 25th percentile of PM2.5 during the Monsoon season of 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6314,7899,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city registered the peak 25th percentile of PM2.5 throughout the Winter season of 2019.,Vrindavan 6315,7900,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state that showed the lowest average PM10 over the Winter season of 2021.,Meghalaya 6316,7901,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station possessed the lowest 75th percentile for PM2.5 in the Monsoon season of 2021?,"Ratanpura, Rupnagar - Ambuja Cements" 6317,7903,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city experienced the third lowest average PM10 throughout the Winter season of 2020.,Shillong 6318,7904,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state that recorded the 3rd lowest 75th percentile of PM10 over the Post-Monsoon season of 2021.,Puducherry 6319,7905,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 25th percentile for PM10 in the Winter season of 2022?,Delhi 6320,7907,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest median PM10 throughout the Winter season of 2020.,Virar 6321,7909,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 75th percentile for PM2.5 in the Winter season of 2018?,Uttarakhand 6322,7910,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that showed the lowest 75th percentile of PM10 during the Summer season of 2021.,Kolar 6323,7911,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal median PM2.5 throughout the Winter season of 2022.,Nandesari 6324,7912,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM10 over the Post-Monsoon season of 2024.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6325,7913,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state experienced the 3rd highest 25th percentile for PM10 in the Winter season of 2023?,Himachal Pradesh 6326,7914,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the third highest 25th percentile of PM10 during the Summer season of 2020.,Sikkim 6327,7915,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state registered the 2nd highest 75th percentile of PM10 throughout the Summer season of 2019.,Tripura 6328,7916,spatio_temporal_aggregation,Which station had the highest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the peak median PM10 over the Summer season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 6329,7917,spatio_temporal_aggregation,Which station had the highest average PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station possessed the highest average for PM10 in the Winter season of 2020?,"Zero Point GICI, Gangtok - SSPCB" 6330,7918,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station exhibiting the 2nd lowest 75th percentile of PM2.5 during the Monsoon season of 2024.,"Diwator Nagar, Koppal - KSPCB" 6331,7919,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report which city experienced the most minimal 25th percentile of PM2.5 throughout the Winter season of 2023.,Aizawl 6332,7920,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station that recorded the lowest average PM10 over the Winter season of 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 6333,7921,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city showed the 3rd lowest 25th percentile for PM2.5 in the Post-Monsoon season of 2022?,Bhilai 6334,7922,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest median PM10 during the Winter season of 2024.,Nagapattinam 6335,7923,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest median PM2.5 throughout the Winter season of 2022.,Vijayawada 6336,7924,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the most minimal average PM2.5 over the Monsoon season of 2022.,Aizawl 6337,7925,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 75th percentile for PM2.5 in the Winter season of 2020?,"Udyogamandal, Eloor - Kerala PCB" 6338,7926,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest median PM2.5 during the Monsoon season of 2021.,Meghalaya 6339,7927,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest average PM2.5 throughout the Monsoon season of 2024.,Delhi 6340,7929,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state experienced the 2nd highest 25th percentile for PM10 in the Post-Monsoon season of 2018?,Tripura 6341,7932,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station that showed the lowest average PM10 over the Monsoon season of 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 6342,7933,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state possessed the 3rd highest average for PM10 in the Summer season of 2021?,Manipur 6343,7934,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city exhibiting the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2023.,Silchar 6344,7936,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city that recorded the peak 25th percentile of PM2.5 over the Summer season of 2023.,Virudhunagar 6345,7937,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state showed the lowest median for PM10 in the Summer season of 2018?,Kerala 6346,7938,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that registered the third highest 75th percentile of PM10 during the Post-Monsoon season of 2023.,"Velippalayam, Nagapattinam - TNPCB" 6347,7939,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest median PM2.5 throughout the Winter season of 2022.,Virar 6348,7940,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the peak 25th percentile of PM10 over the Post-Monsoon season of 2020.,Vrindavan 6349,7941,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest 25th percentile for PM2.5 in the Winter season of 2022?,Aizawl 6350,7945,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city experienced the 2nd highest 25th percentile for PM10 in the Monsoon season of 2022?,Virar 6351,7946,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the third highest median PM10 during the Winter season of 2021.,Vijayawada 6352,7947,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest median PM10 throughout the Post-Monsoon season of 2022.,"Vikas Sadan, Gurugram - HSPCB" 6353,7948,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that showed the 2nd highest 25th percentile of PM10 over the Winter season of 2019.,Tripura 6354,7949,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city possessed the 3rd highest 75th percentile for PM10 in the Post-Monsoon season of 2020?,Virar 6355,7950,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city exhibiting the 2nd highest average PM10 during the Winter season of 2024.,Panchkula 6356,7951,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city experienced the peak median PM2.5 throughout the Monsoon season of 2019.,Yadgir 6357,7952,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city that recorded the 3rd lowest average PM2.5 over the Monsoon season of 2023.,Aizawl 6358,7953,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest average for PM10 in the Post-Monsoon season of 2022?,Vijayawada 6359,7954,spatio_temporal_aggregation,Which state had the highest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the peak median PM10 during the Winter season of 2018.,Uttarakhand 6360,7956,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 75th percentile of PM2.5 over the Winter season of 2021.,Manipur 6361,7957,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest 25th percentile for PM10 in the Post-Monsoon season of 2018?,Uttarakhand 6362,7958,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that showed the third highest median PM2.5 during the Summer season of 2023.,Ulhasnagar 6363,7959,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest 25th percentile of PM2.5 throughout the Winter season of 2019.,Virudhunagar 6364,7960,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest average PM10 over the Post-Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6365,7962,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that recorded the second highest median PM2.5 during the Post-Monsoon season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6366,7963,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report which state registered the peak 75th percentile of PM10 throughout the Monsoon season of 2024.,Delhi 6367,7965,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest median for PM10 in the Post-Monsoon season of 2023?,Gangtok 6368,7966,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station exhibiting the third highest average PM10 during the Monsoon season of 2021.,"Vikas Sadan, Gurugram - HSPCB" 6369,7967,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state experienced the third lowest average PM10 throughout the Monsoon season of 2018.,Telangana 6370,7968,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that recorded the 3rd highest median PM10 over the Summer season of 2023.,Jharkhand 6371,7969,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest 75th percentile for PM10 in the Summer season of 2018?,"Yerramukkapalli, Kadapa - APPCB" 6372,7970,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second lowest median PM2.5 during the Summer season of 2020.,Andhra Pradesh 6373,7971,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest 75th percentile of PM10 throughout the Monsoon season of 2021.,Manipur 6374,7975,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the most minimal average PM2.5 throughout the Monsoon season of 2019.,"Urban, Chamarajanagar - KSPCB" 6375,7976,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 25th percentile of PM2.5 over the Winter season of 2021.,Virar 6376,7977,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station experienced the 3rd highest average for PM2.5 in the Summer season of 2023?,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 6377,7978,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that recorded the second lowest median PM2.5 during the Winter season of 2018.,Maharashtra 6378,7979,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report which city registered the most minimal 25th percentile of PM10 throughout the Winter season of 2020.,Aizawl 6379,7980,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that showed the 3rd highest 75th percentile of PM2.5 over the Post-Monsoon season of 2021.,Manipur 6380,7981,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station possessed the lowest 75th percentile for PM10 in the Monsoon season of 2020?,"Sikulpuikawn, Aizawl - Mizoram PCB" 6381,7985,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state showed the 3rd highest 75th percentile for PM10 in the Winter season of 2023?,Himachal Pradesh 6382,7986,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 75th percentile of PM10 during the Monsoon season of 2024.,Shillong 6383,7987,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report which station possessed the 2nd most minimal 25th percentile of PM2.5 throughout the Post-Monsoon season of 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6384,7989,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city recorded the 3rd lowest average for PM10 in the Summer season of 2021?,Shillong 6385,7991,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest median PM10 throughout the Monsoon season of 2023.,Himachal Pradesh 6386,7992,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal 25th percentile of PM2.5 over the Monsoon season of 2018.,Tirupati 6387,7993,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station experienced the 2nd highest median for PM10 in the Post-Monsoon season of 2020?,"Yerramukkapalli, Kadapa - APPCB" 6388,7994,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the third highest 25th percentile of PM2.5 during the Post-Monsoon season of 2022.,Vijayawada 6389,7995,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state registered the 2nd most minimal median PM10 throughout the Post-Monsoon season of 2019.,Kerala 6390,7996,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that showed the 3rd highest 75th percentile of PM2.5 over the Monsoon season of 2023.,Tiruchirappalli 6391,7998,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak median PM2.5 during the Winter season of 2018.,Yamuna Nagar 6392,7999,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal median PM2.5 throughout the Summer season of 2024.,Sikkim 6393,8000,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station that recorded the 2nd lowest 25th percentile of PM2.5 over the Summer season of 2019.,"Udyogamandal, Eloor - Kerala PCB" 6394,8001,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state showed the 3rd lowest average for PM2.5 in the Post-Monsoon season of 2018?,Maharashtra 6395,8002,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that registered the peak 25th percentile of PM2.5 during the Summer season of 2019.,Yadgir 6396,8003,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal average PM10 throughout the Winter season of 2019.,Chamarajanagar 6397,8004,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal 25th percentile of PM10 over the Winter season of 2024.,"Manipur University, Imphal - Manipur PCB" 6398,8007,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak average PM2.5 throughout the Summer season of 2018.,Yamuna Nagar 6399,8008,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal median PM10 over the Post-Monsoon season of 2018.,Thiruvananthapuram 6400,8009,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station experienced the lowest 25th percentile for PM10 in the Summer season of 2023?,"GIDC, Nandesari - Nandesari Ind. Association" 6401,8011,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city registered the 2nd most minimal median PM10 throughout the Post-Monsoon season of 2022.,Madikeri 6402,8012,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that showed the 2nd highest 25th percentile of PM10 over the Post-Monsoon season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6403,8014,spatio_temporal_aggregation,Which station had the highest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station exhibiting the peak median PM10 during the Summer season of 2018.,"Zero Point GICI, Gangtok - SSPCB" 6404,8015,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city experienced the third highest average PM2.5 throughout the Monsoon season of 2022.,Vijayawada 6405,8016,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the 2nd most minimal 25th percentile of PM10 over the Monsoon season of 2019.,Jharkhand 6406,8017,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the 2nd lowest 25th percentile for PM10 in the Summer season of 2021?,"Tamaka Ind. Area, Kolar - KSPCB" 6407,8018,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that registered the third highest 25th percentile of PM10 during the Monsoon season of 2022.,Vijayawada 6408,8019,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 75th percentile of PM10 throughout the Post-Monsoon season of 2023.,Gangtok 6409,8020,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal median PM10 over the Monsoon season of 2022.,"Brahmagiri, Udupi - KSPCB" 6410,8022,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that showed the second highest 75th percentile of PM10 during the Monsoon season of 2023.,"Velippalayam, Nagapattinam - TNPCB" 6411,8023,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2020.,"Vijay Nagar, Sangli - MPCB" 6412,8025,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station experienced the 2nd highest 75th percentile for PM10 in the Post-Monsoon season of 2018?,"Yerramukkapalli, Kadapa - APPCB" 6413,8026,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the second lowest median PM2.5 during the Summer season of 2019.,Satna 6414,8027,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city registered the 2nd most minimal 75th percentile of PM2.5 throughout the Winter season of 2020.,Eloor 6415,8028,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city that showed the lowest average PM10 over the Winter season of 2024.,Tirunelveli 6416,8029,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station possessed the 2nd highest average for PM2.5 in the Summer season of 2021?,"Yerramukkapalli, Kadapa - APPCB" 6417,8030,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city exhibiting the 2nd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2023.,Tiruchirappalli 6418,8031,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state experienced the lowest 25th percentile of PM10 throughout the Monsoon season of 2020.,Mizoram 6419,8033,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station showed the 3rd highest 75th percentile for PM2.5 in the Summer season of 2023?,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 6420,8034,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the most minimal average PM10 during the Post-Monsoon season of 2024.,Gangtok 6421,8035,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest average PM10 throughout the Winter season of 2018.,Kerala 6422,8036,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd most minimal 75th percentile of PM10 over the Monsoon season of 2024.,Manipur 6423,8037,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile for PM2.5 in the Winter season of 2018?,Maharashtra 6424,8038,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that showed the lowest 75th percentile of PM2.5 during the Summer season of 2019.,Eloor 6425,8039,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal median PM2.5 throughout the Post-Monsoon season of 2022.,Sikkim 6426,8040,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest 75th percentile of PM10 over the Summer season of 2022.,"Vikas Sadan, Gurugram - HSPCB" 6427,8041,spatio_temporal_aggregation,Which city had the 3rd lowest median PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city experienced the 3rd lowest median for PM10 in the Post-Monsoon season of 2018?,Tirupati 6428,8043,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city registered the peak 25th percentile of PM2.5 throughout the Summer season of 2022.,Virudhunagar 6429,8044,spatio_temporal_aggregation,Which city had the highest average PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city that showed the highest average PM10 over the Post-Monsoon season of 2022.,Virudhunagar 6430,8045,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state possessed the highest 25th percentile for PM10 in the Post-Monsoon season of 2021?,Uttarakhand 6431,8046,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city exhibiting the third lowest 75th percentile of PM10 during the Post-Monsoon season of 2021.,Aizawl 6432,8047,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state experienced the peak 75th percentile of PM2.5 throughout the Monsoon season of 2018.,Uttarakhand 6433,8049,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest median for PM2.5 in the Post-Monsoon season of 2019?,"Yerramukkapalli, Kadapa - APPCB" 6434,8050,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that registered the most minimal median PM10 during the Summer season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6435,8051,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest average PM10 throughout the Summer season of 2023.,Ulhasnagar 6436,8052,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd most minimal median PM2.5 over the Post-Monsoon season of 2020.,Kerala 6437,8056,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the most minimal average PM2.5 over the Post-Monsoon season of 2022.,Mizoram 6438,8057,spatio_temporal_aggregation,Which city had the highest median PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest median for PM10 in the Monsoon season of 2019?,Yadgir 6439,8058,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the most minimal 25th percentile of PM10 during the Winter season of 2021.,"Lumpyngngad, Shillong - Meghalaya PCB" 6440,8059,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station registered the 3rd most minimal average PM2.5 throughout the Summer season of 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 6441,8061,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city possessed the 3rd highest 25th percentile for PM10 in the Summer season of 2022?,Vijayawada 6442,8063,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station experienced the 2nd most minimal average PM10 throughout the Winter season of 2020.,"Urban, Chamarajanagar - KSPCB" 6443,8064,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the 2nd lowest average PM2.5 over the Winter season of 2022.,Chhattisgarh 6444,8065,spatio_temporal_aggregation,Which state had the 2nd lowest average PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest average for PM10 in the Monsoon season of 2022?,Mizoram 6445,8066,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the peak 75th percentile of PM10 during the Monsoon season of 2022.,Jharkhand 6446,8067,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest 75th percentile of PM2.5 throughout the Winter season of 2021.,Vijayawada 6447,8068,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal average PM2.5 over the Post-Monsoon season of 2019.,Tirupati 6448,8070,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that showed the most minimal average PM10 during the Summer season of 2022.,"ECIL Kapra, Hyderabad - TSPCB" 6449,8071,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the lowest median PM2.5 throughout the Winter season of 2021.,"Bandra, Mumbai - MPCB" 6450,8072,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest median PM10 over the Post-Monsoon season of 2021.,"Vikas Sadan, Gurugram - HSPCB" 6451,8073,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Which city experienced the 2nd highest 75th percentile for PM10 in the Winter season of 2021?,Virar 6452,8074,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that recorded the second highest 25th percentile of PM2.5 during the Summer season of 2018.,Tripura 6453,8076,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state that showed the 3rd highest 75th percentile of PM2.5 over the Post-Monsoon season of 2018.,Sikkim 6454,8077,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest average for PM2.5 in the Winter season of 2023?,Udupi 6455,8078,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Identify the station exhibiting the third highest average PM2.5 during the Post-Monsoon season of 2023.,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 6456,8079,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station experienced the third highest 25th percentile of PM2.5 throughout the Summer season of 2021.,"Vijay Nagar, Sangli - MPCB" 6457,8082,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest 75th percentile of PM2.5 during the Monsoon season of 2024.,Thanjavur 6458,8083,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest 25th percentile of PM10 throughout the Post-Monsoon season of 2022.,"Vikas Sadan, Gurugram - HSPCB" 6459,8084,spatio_temporal_aggregation,Which station had the 2nd lowest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd most minimal median PM2.5 over the Monsoon season of 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6460,8085,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station recorded the 2nd highest median for PM10 in the Winter season of 2024?,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6461,8086,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest median PM10 during the Winter season of 2018.,Karnataka 6462,8087,spatio_temporal_aggregation,Which state had the highest average PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Report which state possessed the peak average PM10 throughout the Post-Monsoon season of 2021.,Uttarakhand 6463,8088,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 over the Winter season of 2021.,Virudhunagar 6464,8089,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state experienced the 3rd highest median for PM10 in the Summer season of 2019?,Sikkim 6465,8091,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state registered the 3rd highest median PM10 throughout the Winter season of 2021.,Manipur 6466,8093,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city possessed the lowest median for PM2.5 in the Post-Monsoon season of 2024?,Aizawl 6467,8094,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state exhibiting the 2nd most minimal 75th percentile of PM2.5 during the Winter season of 2019.,Kerala 6468,8095,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station experienced the third highest median PM2.5 throughout the Post-Monsoon season of 2024.,"Sector-116, Noida - UPPCB" 6469,8097,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest 75th percentile for PM2.5 in the Post-Monsoon season of 2024?,Delhi 6470,8098,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that registered the second lowest 75th percentile of PM10 during the Summer season of 2021.,Udupi 6471,8099,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal median PM10 throughout the Monsoon season of 2024.,Meghalaya 6472,8100,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the peak 75th percentile of PM10 over the Monsoon season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 6473,8101,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state recorded the 2nd highest 25th percentile for PM2.5 in the Summer season of 2019?,Tripura 6474,8103,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 75th percentile of PM2.5 throughout the Summer season of 2021.,"Vijay Nagar, Sangli - MPCB" 6475,8105,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city experienced the 3rd lowest median for PM2.5 in the Post-Monsoon season of 2021?,Koppal 6476,8106,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the second lowest 75th percentile of PM10 during the Summer season of 2023.,Nandesari 6477,8107,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station registered the 3rd highest 75th percentile of PM2.5 throughout the Winter season of 2018.,"Worli, Mumbai - MPCB" 6478,8108,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal median PM2.5 over the Winter season of 2021.,Davanagere 6479,8109,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city possessed the highest 75th percentile for PM2.5 in the Winter season of 2023?,Virudhunagar 6480,8111,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state experienced the 2nd highest median PM10 throughout the Summer season of 2024.,Himachal Pradesh 6481,8113,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Which state showed the lowest 25th percentile for PM2.5 in the Summer season of 2021?,Mizoram 6482,8114,spatio_temporal_aggregation,Which city had the 3rd lowest average PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest average for PM2.5 during the Winter season of 2018.,Khanna 6483,8115,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 75th percentile of PM2.5 throughout the Winter season of 2018.,Karnataka 6484,8116,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Determine the city exhibiting the most minimal 25th percentile of PM2.5 over the Summer season of 2023.,Silchar 6485,8117,spatio_temporal_aggregation,Which state had the 2nd lowest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest median for PM10 in the Winter season of 2022?,Mizoram 6486,8118,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Identify the station that showed the third lowest 75th percentile of PM2.5 during the Monsoon season of 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6487,8119,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state possessed the peak 25th percentile of PM2.5 throughout the Summer season of 2023.,Jharkhand 6488,8121,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station experienced the 3rd highest 75th percentile for PM10 in the Summer season of 2020?,"Vikas Sadan, Gurugram - HSPCB" 6489,8122,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the second highest average PM2.5 during the Winter season of 2019.,Virudhunagar 6490,8123,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state registered the 3rd most minimal 75th percentile of PM2.5 throughout the Summer season of 2023.,Arunachal Pradesh 6491,8124,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that showed the 2nd highest average PM10 over the Post-Monsoon season of 2018.,Tripura 6492,8125,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station possessed the 2nd highest median for PM2.5 in the Monsoon season of 2022?,"Vijay Nagar, Sangli - MPCB" 6493,8126,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state exhibiting the third highest average PM2.5 during the Summer season of 2021.,Manipur 6494,8127,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal 75th percentile of PM10 throughout the Monsoon season of 2018.,Maharashtra 6495,8128,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that recorded the peak 75th percentile of PM2.5 over the Post-Monsoon season of 2018.,Uttarakhand 6496,8129,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 25th percentile for PM2.5 in the Monsoon season of 2018?,Tripura 6497,8130,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that registered the third lowest 25th percentile of PM10 during the Summer season of 2022.,"Zero Point GICI, Gangtok - SSPCB" 6498,8131,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 25th percentile of PM10 throughout the Post-Monsoon season of 2022.,Sikkim 6499,8132,spatio_temporal_aggregation,Which city had the highest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the peak median PM2.5 over the Post-Monsoon season of 2018.,Yamuna Nagar 6500,8133,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest average for PM2.5 in the Summer season of 2022?,"ECIL Kapra, Hyderabad - TSPCB" 6501,8134,spatio_temporal_aggregation,Which state had the lowest median PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state that showed the most minimal median PM10 during the Post-Monsoon season of 2019.,Meghalaya 6502,8135,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third lowest 75th percentile of PM10 throughout the Monsoon season of 2022.,Arunachal Pradesh 6503,8136,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest median PM2.5 over the Post-Monsoon season of 2018.,Sikkim 6504,8137,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station experienced the 3rd highest median for PM10 in the Summer season of 2024?,"Velippalayam, Nagapattinam - TNPCB" 6505,8138,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third highest 25th percentile of PM10 during the Summer season of 2021.,"Vikas Sadan, Gurugram - HSPCB" 6506,8139,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest average PM2.5 throughout the Winter season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6507,8141,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station possessed the 3rd highest median for PM2.5 in the Monsoon season of 2018?,"Worli, Mumbai - MPCB" 6508,8143,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal 75th percentile of PM2.5 throughout the Post-Monsoon season of 2020.,Eloor 6509,8144,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station that recorded the most minimal 25th percentile of PM10 over the Winter season of 2023.,"GIDC, Nandesari - Nandesari Ind. Association" 6510,8146,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that registered the third highest 75th percentile of PM10 during the Winter season of 2021.,Vijayawada 6511,8147,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station possessed the 2nd most minimal average PM10 throughout the Post-Monsoon season of 2019.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 6512,8148,spatio_temporal_aggregation,Which station had the lowest average PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the lowest average PM10 over the Summer season of 2023.,"Brahmagiri, Udupi - KSPCB" 6513,8149,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city recorded the 3rd highest 75th percentile for PM2.5 in the Winter season of 2020?,Virar 6514,8150,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that showed the second lowest 25th percentile of PM2.5 during the Winter season of 2022.,Tamil Nadu 6515,8151,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal median PM2.5 throughout the Winter season of 2018.,Chandrapur 6516,8152,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal 25th percentile of PM2.5 over the Monsoon season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6517,8153,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest 75th percentile for PM2.5 in the Monsoon season of 2022?,Virudhunagar 6518,8155,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report which station registered the 3rd highest 25th percentile of PM10 throughout the Monsoon season of 2022.,"Vijay Nagar, Sangli - MPCB" 6519,8156,spatio_temporal_aggregation,Which state had the highest median PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that showed the peak median PM10 over the Summer season of 2020.,Uttarakhand 6520,8158,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station exhibiting the peak 75th percentile of PM10 during the Winter season of 2024.,"Vikas Sadan, Gurugram - HSPCB" 6521,8159,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state experienced the third most minimal median PM2.5 throughout the Summer season of 2021.,Arunachal Pradesh 6522,8161,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest median for PM10 in the Monsoon season of 2023?,Delhi 6523,8162,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third lowest median PM10 during the Summer season of 2019.,Andhra Pradesh 6524,8163,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city possessed the third most minimal 25th percentile of PM10 throughout the Monsoon season of 2023.,Bidar 6525,8164,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Determine the city exhibiting the highest 75th percentile of PM10 over the Post-Monsoon season of 2018.,Yamuna Nagar 6526,8165,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest median for PM10 in the Post-Monsoon season of 2023?,"Tarapur, Silchar - PCBA" 6527,8167,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak 25th percentile of PM2.5 throughout the Monsoon season of 2021.,Virudhunagar 6528,8168,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd lowest 75th percentile of PM10 over the Summer season of 2024.,Eloor 6529,8169,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Which state experienced the 2nd highest average PM2.5 in the Winter season of 2023?,Bihar 6530,8170,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the third highest 75th percentile of PM2.5 during the Summer season of 2023.,Ulhasnagar 6531,8171,spatio_temporal_aggregation,Which state had the 3rd highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state registered the third highest median PM2.5 throughout the Monsoon season of 2022.,Himachal Pradesh 6532,8173,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city possessed the lowest median for PM2.5 in the Monsoon season of 2024?,Aizawl 6533,8175,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city experienced the 2nd highest median PM10 throughout the Winter season of 2021.,Virar 6534,8176,spatio_temporal_aggregation,Which city had the lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city that recorded the most minimal median PM10 over the Winter season of 2018.,Kolar 6535,8177,spatio_temporal_aggregation,Which state had the 2nd highest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state showed the 2nd highest 25th percentile for PM10 in the Summer season of 2021?,Sikkim 6536,8178,spatio_temporal_aggregation,Which city had the 2nd highest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest median PM2.5 during the Monsoon season of 2022.,Virar 6537,8179,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal 75th percentile of PM2.5 throughout the Summer season of 2022.,Vijayapura 6538,8180,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest 75th percentile of PM10 over the Post-Monsoon season of 2023.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6539,8181,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station recorded the 3rd lowest 75th percentile for PM2.5 in the Summer season of 2021?,"Velachery Res. Area, Chennai - CPCB" 6540,8182,spatio_temporal_aggregation,Which state had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that showed the third highest 75th percentile of PM10 during the Post-Monsoon season of 2019.,Sikkim 6541,8183,spatio_temporal_aggregation,Which station had the highest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the peak median PM2.5 throughout the Monsoon season of 2024.,"Vyttila, Kochi - Kerala PCB" 6542,8184,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd most minimal median PM2.5 over the Post-Monsoon season of 2024.,"Kumaran College, Tirupur - TNPCB" 6543,8185,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station experienced the 2nd lowest median for PM10 in the Winter season of 2019?,"Udyogamandal, Eloor - Kerala PCB" 6544,8187,spatio_temporal_aggregation,Which station had the 3rd highest median PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station registered the 3rd highest median PM2.5 throughout the Post-Monsoon season of 2021.,"Vijay Nagar, Sangli - MPCB" 6545,8188,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that showed the 2nd highest 75th percentile of PM10 over the Winter season of 2023.,Rohtak 6546,8189,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city possessed the 3rd highest average for PM10 in the Monsoon season of 2018?,Vrindavan 6547,8190,spatio_temporal_aggregation,Which state had the highest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state exhibiting the peak average PM10 during the Monsoon season of 2018.,Uttarakhand 6548,8191,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report which station experienced the most minimal median PM10 throughout the Post-Monsoon season of 2020.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6549,8192,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the 2nd most minimal 25th percentile of PM2.5 over the Monsoon season of 2020.,Meghalaya 6550,8194,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 25th percentile of PM2.5 during the Summer season of 2021.,Manipur 6551,8195,spatio_temporal_aggregation,Which station had the highest median PM10 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the peak median PM10 throughout the Summer season of 2024.,"Vikas Sadan, Gurugram - HSPCB" 6552,8196,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest average PM2.5 over the Post-Monsoon season of 2023.,Haryana 6553,8198,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Identify the city that showed the second lowest average PM2.5 during the Winter season of 2020.,Eloor 6554,8200,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest average PM10 over the Monsoon season of 2018.,Yadgir 6555,8202,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the most minimal median PM2.5 during the Summer season of 2023.,"Tarapur, Silchar - PCBA" 6556,8203,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report which station registered the most minimal median PM10 throughout the Summer season of 2023.,"Brahmagiri, Udupi - KSPCB" 6557,8204,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station that showed the 3rd lowest 25th percentile of PM2.5 over the Post-Monsoon season of 2023.,"Tarapur, Silchar - PCBA" 6558,8205,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city possessed the 3rd lowest 25th percentile for PM10 in the Post-Monsoon season of 2022?,Ernakulam 6559,8207,spatio_temporal_aggregation,Which state had the 3rd lowest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state experienced the third lowest average PM2.5 throughout the Post-Monsoon season of 2022.,Meghalaya 6560,8208,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state that recorded the lowest average PM10 over the Winter season of 2024.,Sikkim 6561,8209,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest 25th percentile for PM2.5 in the Post-Monsoon season of 2019?,Eloor 6562,8210,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that registered the second lowest 75th percentile of PM2.5 during the Summer season of 2023.,"OP Jindal School, Kunjemura - CECB" 6563,8211,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station possessed the third highest 75th percentile of PM2.5 throughout the Summer season of 2024.,"VOC Nagar_SIPCOT, Ranipet - TNPCB" 6564,8212,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest 25th percentile of PM2.5 over the Winter season of 2023.,Tripura 6565,8213,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state recorded the lowest 25th percentile for PM10 in the Summer season of 2018?,Kerala 6566,8214,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Identify the station that showed the second lowest 75th percentile of PM10 during the Summer season of 2019.,"Manali Village, Chennai - TNPCB" 6567,8215,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest 75th percentile of PM10 throughout the Post-Monsoon season of 2021.,Sikkim 6568,8216,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the most minimal 75th percentile of PM10 over the Summer season of 2022.,Sikkim 6569,8217,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state experienced the 2nd highest median for PM10 in the Winter season of 2018?,Tripura 6570,8218,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the peak 25th percentile of PM2.5 during the Winter season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 6571,8219,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state registered the 2nd highest average PM2.5 throughout the Winter season of 2024.,Himachal Pradesh 6572,8220,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Determine the city that showed the lowest 75th percentile of PM10 over the Monsoon season of 2024.,Koppal 6573,8221,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest 25th percentile for PM2.5 in the Summer season of 2021?,Koppal 6574,8223,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state experienced the 2nd highest 75th percentile of PM10 throughout the Monsoon season of 2021.,Sikkim 6575,8225,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest 25th percentile for PM2.5 in the Summer season of 2024?,Tirunelveli 6576,8227,spatio_temporal_aggregation,Which state had the 3rd lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third lowest average PM10 throughout the Monsoon season of 2019.,Jharkhand 6577,8228,spatio_temporal_aggregation,Which city had the 3rd highest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city exhibiting the 3rd highest median PM10 over the Winter season of 2019.,Virar 6578,8230,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that showed the peak 25th percentile of PM2.5 during the Post-Monsoon season of 2018.,Uttarakhand 6579,8231,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report which city possessed the lowest median PM2.5 throughout the Monsoon season of 2020.,Aizawl 6580,8232,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station exhibiting the peak 25th percentile of PM2.5 over the Winter season of 2024.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6581,8233,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station experienced the most minimal median PM10 in the Summer season of 2018?,"Victoria, Kolkata - WBPCB" 6582,8234,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the second highest average PM2.5 during the Summer season of 2024.,Tiruchirappalli 6583,8235,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the 2nd highest average PM2.5 throughout the Winter season of 2023.,Thoothukudi 6584,8236,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state that showed the 3rd most minimal 75th percentile of PM10 over the Monsoon season of 2021.,Tripura 6585,8238,spatio_temporal_aggregation,Which state had the highest 25th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state exhibiting the peak 25th percentile of PM2.5 during the Monsoon season of 2023.,Manipur 6586,8239,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal average PM2.5 throughout the Winter season of 2023.,Jammu and Kashmir 6587,8240,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that recorded the highest 75th percentile of PM10 over the Post-Monsoon season of 2018.,Uttarakhand 6588,8241,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city showed the 3rd lowest median for PM2.5 in the Monsoon season of 2024?,Koppal 6589,8243,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third lowest median PM10 throughout the Summer season of 2022.,Jammu and Kashmir 6590,8245,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average for PM10 in the Summer season of 2021?,Gadag 6591,8247,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city possessed the third highest median PM2.5 throughout the Summer season of 2018.,Vrindavan 6592,8249,spatio_temporal_aggregation,Which city had the highest median PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city experienced the highest median for PM10 in the Monsoon season of 2023?,Virudhunagar 6593,8250,spatio_temporal_aggregation,Which city had the 2nd lowest 75th percentile of PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the second lowest 75th percentile of PM10 during the Monsoon season of 2019.,Eloor 6594,8252,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city that showed the 2nd most minimal median PM2.5 over the Summer season of 2018.,Rajamahendravaram 6595,8253,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station possessed the 3rd lowest 75th percentile for PM10 in the Winter season of 2022?,"Lumpyngngad, Shillong - Meghalaya PCB" 6596,8254,spatio_temporal_aggregation,Which city had the highest average PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak average PM10 during the Winter season of 2019.,Vrindavan 6597,8255,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station experienced the third highest 75th percentile of PM2.5 throughout the Winter season of 2021.,"Vijay Nagar, Sangli - MPCB" 6598,8256,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that recorded the 2nd highest 25th percentile of PM2.5 over the Monsoon season of 2024.,Thanjavur 6599,8257,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 75th percentile for PM2.5 in the Winter season of 2020?,"Bandhavgar Colony, Satna - Birla Cement" 6600,8258,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that registered the lowest 75th percentile of PM2.5 during the Summer season of 2023.,Sikkim 6601,8259,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Report which station possessed the 2nd most minimal 25th percentile of PM2.5 throughout the Summer season of 2018.,"Kendriya Vidyalaya, Lucknow - CPCB" 6602,8260,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the peak 75th percentile of PM10 over the Monsoon season of 2023.,Manipur 6603,8261,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state recorded the highest average for PM2.5 in the Post-Monsoon season of 2023?,Delhi 6604,8262,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that showed the second highest median PM2.5 during the Monsoon season of 2019.,"Yerramukkapalli, Kadapa - APPCB" 6605,8266,spatio_temporal_aggregation,Which city had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that recorded the second highest 25th percentile of PM2.5 during the Post-Monsoon season of 2020.,Virudhunagar 6606,8267,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Report which city registered the 2nd most minimal average PM2.5 throughout the Summer season of 2019.,Satna 6607,8268,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city that showed the 3rd lowest 75th percentile of PM2.5 over the Summer season of 2021.,Koppal 6608,8271,spatio_temporal_aggregation,Which city had the 2nd lowest median PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal median PM10 throughout the Monsoon season of 2022.,Gangtok 6609,8272,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the 2nd highest 75th percentile of PM10 over the Winter season of 2022.,"Vikas Sadan, Gurugram - HSPCB" 6610,8273,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest 25th percentile for PM2.5 in the Winter season of 2019?,Virar 6611,8274,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that registered the 2nd most minimal average PM10 during the Post-Monsoon season of 2018.,Chikkaballapur 6612,8275,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station possessed the third lowest 25th percentile of PM2.5 throughout the Monsoon season of 2022.,"Zero Point GICI, Gangtok - SSPCB" 6613,8276,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal median PM10 over the Summer season of 2021.,"Brahmagiri, Udupi - KSPCB" 6614,8277,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Which station recorded the lowest median for PM2.5 in the Monsoon season of 2021?,"Ratanpura, Rupnagar - Ambuja Cements" 6615,8278,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that showed the second lowest 25th percentile of PM2.5 during the Monsoon season of 2023.,"Tarapur, Silchar - PCBA" 6616,8279,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station possessed the third lowest 75th percentile of PM2.5 throughout the Summer season of 2023.,"Manoharpur, Agra - UPPCB" 6617,8280,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal 25th percentile of PM2.5 over the Post-Monsoon season of 2021.,Meghalaya 6618,8282,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that recorded the second lowest average PM10 during the Post-Monsoon season of 2022.,Gangtok 6619,8283,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city registered the peak 75th percentile of PM10 throughout the Winter season of 2019.,Vrindavan 6620,8284,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that showed the 3rd highest 75th percentile of PM2.5 over the Post-Monsoon season of 2019.,"Vyttila, Kochi - Kerala PCB" 6621,8285,spatio_temporal_aggregation,Which city had the 3rd lowest average PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city possessed the 3rd lowest average for PM10 in the Winter season of 2023?,Madikeri 6622,8286,spatio_temporal_aggregation,Which state had the lowest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Identify the state exhibiting the most minimal average PM10 during the Summer season of 2019.,Tamil Nadu 6623,8287,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city experienced the 2nd most minimal 25th percentile of PM10 throughout the Monsoon season of 2023.,Koppal 6624,8288,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station that recorded the 3rd lowest 75th percentile of PM10 over the Summer season of 2022.,"Brahmagiri, Udupi - KSPCB" 6625,8289,spatio_temporal_aggregation,Which state had the highest average PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the highest average for PM10 in the Post-Monsoon season of 2022?,Delhi 6626,8291,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the lowest 75th percentile of PM2.5 throughout the Post-Monsoon season of 2019.,"Udyogamandal, Eloor - Kerala PCB" 6627,8292,spatio_temporal_aggregation,Which state had the 2nd lowest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Determine the state exhibiting the 2nd most minimal average PM2.5 over the Summer season of 2019.,Andhra Pradesh 6628,8293,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Which station recorded the 3rd highest average for PM10 in the Monsoon season of 2024?,"Vijay Nagar Scheme-78, Indore - Glenmark" 6629,8294,spatio_temporal_aggregation,Which city had the 2nd highest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that showed the second highest median PM10 during the Summer season of 2022.,Virar 6630,8295,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third most minimal median PM2.5 throughout the Monsoon season of 2021.,Arunachal Pradesh 6631,8296,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest median PM10 over the Monsoon season of 2020.,"Hebbal 1st Stage, Mysuru - KSPCB" 6632,8297,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station experienced the 2nd highest 25th percentile for PM10 in the Post-Monsoon season of 2019?,"Yerramukkapalli, Kadapa - APPCB" 6633,8298,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that recorded the lowest 75th percentile of PM10 during the Post-Monsoon season of 2024.,Gangtok 6634,8299,spatio_temporal_aggregation,Which state had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Report which state registered the most minimal 25th percentile of PM2.5 throughout the Post-Monsoon season of 2018.,Kerala 6635,8301,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state possessed the 3rd highest median for PM10 in the Winter season of 2020?,Puducherry 6636,8302,spatio_temporal_aggregation,Which city had the 3rd highest average PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city exhibiting the third highest average PM2.5 during the Monsoon season of 2021.,Vijayawada 6637,8303,spatio_temporal_aggregation,Which state had the 2nd lowest median PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Report which state experienced the 2nd most minimal median PM2.5 throughout the Post-Monsoon season of 2018.,Karnataka 6638,8305,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the 3rd lowest 75th percentile for PM10 in the Monsoon season of 2021?,"Panchal Nagar, Gadag - KSPCB" 6639,8306,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that registered the third highest 25th percentile of PM2.5 during the Monsoon season of 2021.,Manipur 6640,8307,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak 75th percentile of PM10 throughout the Winter season of 2024.,Rohtak 6641,8308,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd most minimal 75th percentile of PM2.5 over the Post-Monsoon season of 2020.,Nagaland 6642,8309,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Which city recorded the lowest average for PM10 in the Monsoon season of 2024?,Koppal 6643,8310,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that showed the peak 75th percentile of PM2.5 during the Monsoon season of 2022.,"Yerramukkapalli, Kadapa - APPCB" 6644,8311,spatio_temporal_aggregation,Which state had the 2nd highest median PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest median PM10 throughout the Post-Monsoon season of 2018.,Tripura 6645,8312,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Determine the state exhibiting the highest 75th percentile of PM10 over the Summer season of 2020.,Uttarakhand 6646,8314,spatio_temporal_aggregation,Which city had the highest average PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the peak average PM10 during the Summer season of 2018.,Yamuna Nagar 6647,8315,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Report which city registered the 3rd highest 75th percentile of PM10 throughout the Post-Monsoon season of 2019.,Virar 6648,8316,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state that showed the 3rd lowest 75th percentile of PM10 over the Winter season of 2019.,Kerala 6649,8318,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak 25th percentile of PM2.5 during the Winter season of 2020.,Vrindavan 6650,8319,spatio_temporal_aggregation,Which state had the highest average PM2.5 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Report which state experienced the peak average PM2.5 throughout the Post-Monsoon season of 2022.,Jharkhand 6651,8320,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station that recorded the 3rd lowest average PM10 over the Monsoon season of 2021.,"Panchal Nagar, Gadag - KSPCB" 6652,8321,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state showed the lowest 75th percentile for PM10 in the Winter season of 2023?,Arunachal Pradesh 6653,8323,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the most minimal average PM2.5 throughout the Summer season of 2019.,"Udyogamandal, Eloor - Kerala PCB" 6654,8324,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd most minimal median PM10 over the Winter season of 2022.,"Lumpyngngad, Shillong - Meghalaya PCB" 6655,8325,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 75th percentile for PM10 in the Post-Monsoon season of 2020?,Meghalaya 6656,8326,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that showed the third highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024.,Kozhikode 6657,8327,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station possessed the 2nd highest 75th percentile of PM2.5 throughout the Winter season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6658,8329,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city experienced the 3rd highest median for PM2.5 in the Post-Monsoon season of 2020?,Virar 6659,8330,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city that recorded the peak average PM2.5 during the Summer season of 2020.,Vrindavan 6660,8331,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station registered the 2nd most minimal average PM10 throughout the Monsoon season of 2019.,"Udyogamandal, Eloor - Kerala PCB" 6661,8332,spatio_temporal_aggregation,Which station had the 3rd highest median PM10 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that showed the 3rd highest median PM10 over the Post-Monsoon season of 2018.,"Worli, Mumbai - MPCB" 6662,8333,spatio_temporal_aggregation,Which station had the 2nd highest average PM10 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station possessed the 2nd highest average for PM10 in the Monsoon season of 2023?,"Velippalayam, Nagapattinam - TNPCB" 6663,8334,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state exhibiting the 2nd most minimal 25th percentile of PM2.5 during the Post-Monsoon season of 2018.,Karnataka 6664,8335,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Report which state experienced the third highest average PM2.5 throughout the Summer season of 2020.,Sikkim 6665,8336,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Determine the station that recorded the 3rd lowest average PM10 over the Summer season of 2018.,"Anand Kala Kshetram, Rajamahendravaram - APPCB" 6666,8337,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station showed the 2nd highest 75th percentile for PM2.5 in the Monsoon season of 2018?,"Yerramukkapalli, Kadapa - APPCB" 6667,8338,spatio_temporal_aggregation,Which station had the 2nd highest median PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second highest median PM2.5 during the Monsoon season of 2023.,"Vasundhara Nagar_UIT, Bhiwadi - RSPCB" 6668,8340,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station exhibiting the 2nd most minimal average PM10 over the Monsoon season of 2018.,"Hebbal, Bengaluru - KSPCB" 6669,8341,spatio_temporal_aggregation,Which station had the highest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station recorded the highest median for PM10 in the Summer season of 2022?,"Yerramukkapalli, Kadapa - APPCB" 6670,8343,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Report which state possessed the 2nd most minimal 25th percentile of PM10 throughout the Summer season of 2018.,Andhra Pradesh 6671,8344,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM2.5 over the Winter season of 2022.,Virar 6672,8345,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station experienced the most minimal median PM10 in the Post-Monsoon season of 2022?,"Zero Point GICI, Gangtok - SSPCB" 6673,8346,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Post-Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that recorded the third highest 25th percentile of PM10 during the Post-Monsoon season of 2022.,Jharkhand 6674,8347,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest 25th percentile of PM2.5 throughout the Monsoon season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6675,8348,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the highest 25th percentile of PM10 over the Post-Monsoon season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 6676,8349,spatio_temporal_aggregation,Which station had the 2nd lowest average PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest average for PM2.5 in the Monsoon season of 2020?,"Borivali East, Mumbai - MPCB" 6677,8350,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city exhibiting the third highest 25th percentile of PM2.5 during the Summer season of 2023.,Ulhasnagar 6678,8352,spatio_temporal_aggregation,Which station had the 3rd highest average PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that recorded the 3rd highest average PM10 over the Post-Monsoon season of 2021.,"Vikas Sadan, Gurugram - HSPCB" 6679,8353,spatio_temporal_aggregation,Which station had the 3rd lowest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Which station showed the 3rd lowest average for PM10 in the Summer season of 2021?,"Lumpyngngad, Shillong - Meghalaya PCB" 6680,8354,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the most minimal 25th percentile of PM10 during the Post-Monsoon season of 2020.,Aizawl 6681,8355,spatio_temporal_aggregation,Which state had the 3rd lowest median PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third lowest median PM10 throughout the Monsoon season of 2019.,Jharkhand 6682,8358,spatio_temporal_aggregation,Which station had the highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that showed the peak median PM10 during the Post-Monsoon season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 6683,8359,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the peak 25th percentile of PM10 throughout the Summer season of 2023.,"Vikas Sadan, Gurugram - HSPCB" 6684,8360,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state exhibiting the most minimal average PM2.5 over the Monsoon season of 2023.,Sikkim 6685,8361,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Which state experienced the 2nd lowest 25th percentile for PM10 in the Post-Monsoon season of 2020?,Meghalaya 6686,8363,spatio_temporal_aggregation,Which city had the 2nd highest average PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city registered the 2nd highest average PM10 throughout the Post-Monsoon season of 2024.,Rohtak 6687,8364,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd lowest 75th percentile of PM10 over the Winter season of 2024.,"Municipal Corporation Office, Tirunelveli - TNPCB" 6688,8365,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Which station possessed the 3rd highest 25th percentile for PM2.5 in the Monsoon season of 2019?,"Vyttila, Kochi - Kerala PCB" 6689,8366,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city exhibiting the third lowest 25th percentile of PM10 during the Winter season of 2020.,Chamarajanagar 6690,8368,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest 25th percentile of PM10 over the Monsoon season of 2018.,Vrindavan 6691,8370,spatio_temporal_aggregation,Which station had the 2nd highest median PM10 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second highest median PM10 during the Post-Monsoon season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6692,8373,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Which city recorded the highest 75th percentile for PM10 in the Summer season of 2018?,Yamuna Nagar 6693,8374,spatio_temporal_aggregation,Which station had the 2nd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that showed the second lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2021.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6694,8375,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state possessed the 2nd highest median PM2.5 throughout the Summer season of 2021.,Sikkim 6695,8376,spatio_temporal_aggregation,Which city had the 2nd lowest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Determine the city exhibiting the 2nd most minimal median PM2.5 over the Monsoon season of 2022.,Bhilai 6696,8377,spatio_temporal_aggregation,Which city had the 3rd lowest median PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Which city experienced the 3rd lowest median for PM2.5 in the Summer season of 2020?,Chennai 6697,8378,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that recorded the most minimal median PM2.5 during the Summer season of 2021.,Meghalaya 6698,8380,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station that showed the 3rd lowest median PM2.5 over the Summer season of 2021.,"Devaraj Urs Badavane, Davanagere - KSPCB" 6699,8381,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state possessed the highest 75th percentile for PM10 in the Winter season of 2023?,Delhi 6700,8382,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city exhibiting the 2nd most minimal 25th percentile of PM10 during the Summer season of 2021.,Kolar 6701,8383,spatio_temporal_aggregation,Which city had the 3rd highest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Report which city experienced the third highest median PM2.5 throughout the Monsoon season of 2021.,Vijayawada 6702,8384,spatio_temporal_aggregation,Which state had the 2nd highest average PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Determine the state that recorded the 2nd highest average PM2.5 over the Winter season of 2018.,Tripura 6703,8385,spatio_temporal_aggregation,Which city had the lowest average PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Which city showed the lowest average for PM2.5 in the Monsoon season of 2023?,Silchar 6704,8386,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that registered the second lowest 25th percentile of PM10 during the Winter season of 2024.,Tirunelveli 6705,8387,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Report which station possessed the peak average PM2.5 throughout the Post-Monsoon season of 2019.,"Zero Point GICI, Gangtok - SSPCB" 6706,8388,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city exhibiting the 3rd most minimal 25th percentile of PM2.5 over the Post-Monsoon season of 2020.,Kozhikode 6707,8389,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state recorded the 2nd lowest 25th percentile for PM2.5 in the Winter season of 2019?,Kerala 6708,8390,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city that showed the second lowest 25th percentile of PM10 during the Winter season of 2023.,Udupi 6709,8391,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Report which station possessed the lowest median PM2.5 throughout the Post-Monsoon season of 2023.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6710,8392,spatio_temporal_aggregation,Which station had the 2nd highest average PM10 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station exhibiting the 2nd highest average PM10 over the Winter season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6711,8393,spatio_temporal_aggregation,Which station had the 2nd highest average PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Which station experienced the 2nd highest average for PM2.5 in the Post-Monsoon season of 2019?,"Yerramukkapalli, Kadapa - APPCB" 6712,8394,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city that recorded the third highest 25th percentile of PM2.5 during the Summer season of 2019.,Virudhunagar 6713,8395,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest 25th percentile of PM2.5 throughout the Winter season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6714,8396,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the highest 25th percentile of PM2.5 over the Monsoon season of 2020.,"Zero Point GICI, Gangtok - SSPCB" 6715,8397,spatio_temporal_aggregation,Which city had the 2nd lowest 25th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city possessed the 2nd lowest 25th percentile for PM2.5 in the Summer season of 2018?,Rajamahendravaram 6716,8398,spatio_temporal_aggregation,Which state had the 2nd lowest 25th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state exhibiting the 2nd most minimal 25th percentile of PM10 during the Monsoon season of 2024.,Manipur 6717,8399,spatio_temporal_aggregation,Which station had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station experienced the third highest 75th percentile of PM2.5 throughout the Monsoon season of 2020.,"Vijay Nagar, Sangli - MPCB" 6718,8401,spatio_temporal_aggregation,Which city had the 3rd highest average PM10 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the 3rd highest average for PM10 in the Monsoon season of 2019?,Virudhunagar 6719,8402,spatio_temporal_aggregation,Which state had the lowest median PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that registered the most minimal median PM2.5 during the Monsoon season of 2019.,Chandigarh 6720,8403,spatio_temporal_aggregation,Which city had the highest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak average PM2.5 throughout the Post-Monsoon season of 2024.,Thoothukudi 6721,8404,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Determine the state exhibiting the 3rd highest median PM10 over the Winter season of 2019.,Sikkim 6722,8405,spatio_temporal_aggregation,Which state had the 3rd lowest median PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Which state recorded the 3rd lowest median for PM2.5 in the Summer season of 2019?,Tamil Nadu 6723,8406,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Winter season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that showed the peak median PM2.5 during the Winter season of 2021.,Uttarakhand 6724,8409,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city experienced the 3rd highest 25th percentile for PM2.5 in the Winter season of 2018?,Vrindavan 6725,8410,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Identify the station that recorded the third highest 25th percentile of PM10 during the Summer season of 2020.,"Vikas Sadan, Gurugram - HSPCB" 6726,8412,spatio_temporal_aggregation,Which state had the lowest average PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state that showed the lowest average PM2.5 over the Summer season of 2019.,Kerala 6727,8413,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Which state possessed the lowest 75th percentile for PM10 in the Post-Monsoon season of 2023?,Sikkim 6728,8414,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Identify the city exhibiting the 2nd most minimal average PM10 during the Monsoon season of 2021.,Udupi 6729,8415,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station experienced the third lowest 75th percentile of PM2.5 throughout the Post-Monsoon season of 2020.,"Tamaka Ind. Area, Kolar - KSPCB" 6730,8416,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest 25th percentile of PM2.5 over the Monsoon season of 2024.,Ranipet 6731,8417,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Which city showed the highest 25th percentile for PM2.5 in the Post-Monsoon season of 2021?,Virudhunagar 6732,8418,spatio_temporal_aggregation,Which state had the 2nd highest median PM2.5 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that registered the second highest median PM2.5 during the Winter season of 2020.,Sikkim 6733,8419,spatio_temporal_aggregation,Which city had the 2nd highest average PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Report which city possessed the 2nd highest average PM2.5 throughout the Winter season of 2022.,Virar 6734,8421,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average for PM2.5 in the Summer season of 2023?,Aizawl 6735,8422,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that showed the peak average PM2.5 during the Summer season of 2023.,"Vijay Nagar, Sangli - MPCB" 6736,8423,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station possessed the third most minimal median PM2.5 throughout the Summer season of 2022.,"DM College of Science, Imphal - Manipur PCB" 6737,8424,spatio_temporal_aggregation,Which state had the 3rd lowest 25th percentile of PM10 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Determine the state exhibiting the 3rd lowest 25th percentile of PM10 over the Winter season of 2023.,Jammu and Kashmir 6738,8425,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Which station experienced the 2nd highest 75th percentile for PM10 in the Monsoon season of 2020?,"Yerramukkapalli, Kadapa - APPCB" 6739,8426,spatio_temporal_aggregation,Which station had the 3rd lowest median PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Identify the station that recorded the third lowest median PM10 during the Monsoon season of 2022.,"Stuart Hill, Madikeri - KSPCB" 6740,8427,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Report which station registered the 2nd highest 25th percentile of PM2.5 throughout the Post-Monsoon season of 2024.,"Town Hall - Lal Bagh, Darbhanga - BSPCB" 6741,8428,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Determine the city that showed the 2nd highest 75th percentile of PM2.5 over the Monsoon season of 2018.,Yadgir 6742,8429,spatio_temporal_aggregation,Which state had the 2nd highest average PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Which state possessed the 2nd highest average for PM10 in the Summer season of 2019?,Tripura 6743,8430,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Identify the city exhibiting the third highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023.,Thoothukudi 6744,8432,spatio_temporal_aggregation,Which city had the 3rd highest 25th percentile of PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the 3rd highest 25th percentile of PM10 over the Summer season of 2021.,Virar 6745,8433,spatio_temporal_aggregation,Which state had the 3rd highest average PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""state""]) true_code() ",Which state showed the 3rd highest average for PM2.5 in the Winter season of 2019?,Sikkim 6746,8434,spatio_temporal_aggregation,Which station had the highest average PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the peak average PM2.5 during the Winter season of 2022.,"Yerramukkapalli, Kadapa - APPCB" 6747,8435,spatio_temporal_aggregation,Which city had the 2nd lowest average PM10 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""city""]) true_code() ",Report which city possessed the 2nd most minimal average PM10 throughout the Post-Monsoon season of 2019.,Thiruvananthapuram 6748,8436,spatio_temporal_aggregation,Which station had the lowest 25th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal 25th percentile of PM2.5 over the Post-Monsoon season of 2019.,"Borivali East, Mumbai - MPCB" 6749,8437,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Winter season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Which station recorded the 2nd lowest 25th percentile for PM2.5 in the Winter season of 2022?,"Manali Village, Chennai - TNPCB" 6750,8438,spatio_temporal_aggregation,Which station had the highest average PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that showed the peak average PM10 during the Winter season of 2024.,"Vikas Sadan, Gurugram - HSPCB" 6751,8439,spatio_temporal_aggregation,Which state had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state possessed the third most minimal 75th percentile of PM2.5 throughout the Post-Monsoon season of 2024.,Manipur 6752,8440,spatio_temporal_aggregation,Which station had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest 75th percentile of PM2.5 over the Post-Monsoon season of 2019.,"Plammoodu, Thiruvananthapuram - Kerala PCB" 6753,8441,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city experienced the 3rd highest 75th percentile for PM2.5 in the Post-Monsoon season of 2021?,Vijayawada 6754,8442,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Identify the station that recorded the most minimal average PM2.5 during the Summer season of 2020.,"Udyogamandal, Eloor - Kerala PCB" 6755,8443,spatio_temporal_aggregation,Which station had the 3rd highest average PM2.5 during the Post-Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""station""]) true_code() ",Report which station registered the 3rd highest average PM2.5 throughout the Post-Monsoon season of 2018.,"Worli, Mumbai - MPCB" 6756,8445,spatio_temporal_aggregation,Which station had the 2nd lowest median PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station possessed the 2nd lowest median for PM10 in the Summer season of 2022?,"Kompally Municipal Office, Hyderabad - TSPCB" 6757,8446,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station exhibiting the 2nd highest 75th percentile of PM2.5 during the Summer season of 2018.,"Yerramukkapalli, Kadapa - APPCB" 6758,8447,spatio_temporal_aggregation,Which city had the lowest 25th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report which city experienced the lowest 25th percentile of PM2.5 throughout the Winter season of 2019.,Shillong 6759,8448,spatio_temporal_aggregation,Which station had the lowest median PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station that recorded the most minimal median PM2.5 over the Monsoon season of 2024.,"Sikulpuikawn, Aizawl - Mizoram PCB" 6760,8450,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 25th percentile of PM10 during the Monsoon season of 2024.,Imphal 6761,8451,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state possessed the lowest 75th percentile of PM10 throughout the Winter season of 2020.,Mizoram 6762,8452,spatio_temporal_aggregation,Which station had the 3rd highest 25th percentile of PM10 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station exhibiting the 3rd highest 25th percentile of PM10 over the Summer season of 2019.,"Worli, Mumbai - MPCB" 6763,8453,spatio_temporal_aggregation,Which city had the 2nd lowest average PM2.5 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city recorded the 2nd lowest average for PM2.5 in the Post-Monsoon season of 2024?,Tirupur 6764,8454,spatio_temporal_aggregation,Which state had the lowest 75th percentile of PM2.5 during the Summer season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that showed the lowest 75th percentile of PM2.5 during the Summer season of 2019.,Kerala 6765,8455,spatio_temporal_aggregation,Which state had the 3rd highest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest 25th percentile of PM10 throughout the Monsoon season of 2020.,Sikkim 6766,8456,spatio_temporal_aggregation,Which state had the 2nd highest 75th percentile of PM10 during the Summer season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Determine the state exhibiting the 2nd highest 75th percentile of PM10 over the Summer season of 2020.,Tripura 6767,8457,spatio_temporal_aggregation,Which city had the 3rd highest 75th percentile of PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Which city experienced the 3rd highest 75th percentile for PM2.5 in the Monsoon season of 2022?,Vijayawada 6768,8458,spatio_temporal_aggregation,Which station had the highest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that recorded the peak average PM10 during the Summer season of 2021.,"Zero Point GICI, Gangtok - SSPCB" 6769,8459,spatio_temporal_aggregation,Which city had the highest median PM10 during the Post-Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city registered the peak median PM10 throughout the Post-Monsoon season of 2024.,Vapi 6770,8460,spatio_temporal_aggregation,Which city had the 3rd lowest 75th percentile of PM2.5 during the Post-Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Determine the city that showed the 3rd lowest 75th percentile of PM2.5 over the Post-Monsoon season of 2021.,Shillong 6771,8461,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Monsoon season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state possessed the 2nd lowest 75th percentile for PM2.5 in the Monsoon season of 2024?,Sikkim 6772,8462,spatio_temporal_aggregation,Which city had the highest 75th percentile of PM2.5 during the Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Identify the city exhibiting the peak 75th percentile of PM2.5 during the Monsoon season of 2023.,Virudhunagar 6773,8463,spatio_temporal_aggregation,Which station had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""station""]) true_code() ",Report which station experienced the third lowest 25th percentile of PM10 throughout the Monsoon season of 2020.,"Lal Bahadur Shastri Nagar, Kalaburagi - KSPCB" 6774,8464,spatio_temporal_aggregation,Which station had the highest 75th percentile of PM10 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that recorded the peak 75th percentile of PM10 over the Summer season of 2022.,"Yerramukkapalli, Kadapa - APPCB" 6775,8465,spatio_temporal_aggregation,Which state had the 2nd lowest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state showed the 2nd lowest 75th percentile for PM2.5 in the Summer season of 2021?,Puducherry 6776,8466,spatio_temporal_aggregation,Which city had the lowest median PM2.5 during the Winter season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the most minimal median PM2.5 during the Winter season of 2023.,Aizawl 6777,8467,spatio_temporal_aggregation,Which state had the 3rd highest average PM10 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Report which state possessed the third highest average PM10 throughout the Monsoon season of 2022.,Himachal Pradesh 6778,8471,spatio_temporal_aggregation,Which city had the lowest 75th percentile of PM2.5 during the Winter season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Report which city possessed the lowest 75th percentile of PM2.5 throughout the Winter season of 2019.,Eloor 6779,8472,spatio_temporal_aggregation,Which station had the lowest average PM2.5 during the Summer season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""station""]) true_code() ",Determine the station exhibiting the most minimal average PM2.5 over the Summer season of 2024.,"Bhelupur, Varanasi - UPPCB" 6780,8473,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Which station experienced the highest 25th percentile for PM10 in the Winter season of 2024?,"Vikas Sadan, Gurugram - HSPCB" 6781,8474,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""station""]) true_code() ",Identify the station that recorded the second lowest 25th percentile of PM2.5 during the Monsoon season of 2018.,"Tirumala, Tirupati - APPCB" 6782,8475,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM2.5 during the Summer season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""city""]) true_code() ",Report which city registered the 3rd most minimal 25th percentile of PM2.5 throughout the Summer season of 2022.,Imphal 6783,8476,spatio_temporal_aggregation,Which station had the highest 25th percentile of PM2.5 during the Post-Monsoon season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that showed the highest 25th percentile of PM2.5 over the Post-Monsoon season of 2023.,"Vijay Nagar Scheme-78, Indore - Glenmark" 6784,8477,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station possessed the 3rd lowest average for PM2.5 in the Monsoon season of 2018?,"Hebbal, Bengaluru - KSPCB" 6785,8478,spatio_temporal_aggregation,Which city had the lowest average PM10 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Identify the city exhibiting the most minimal average PM10 during the Summer season of 2021.,Kolar 6786,8481,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city showed the 3rd lowest 25th percentile for PM10 in the Summer season of 2018?,Kolkata 6787,8482,spatio_temporal_aggregation,Which state had the highest 75th percentile of PM10 during the Summer season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""state""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Identify the state that registered the peak 75th percentile of PM10 during the Summer season of 2018.,Uttarakhand 6788,8483,spatio_temporal_aggregation,Which city had the highest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""city""]) true_code() ",Report which city possessed the peak 25th percentile of PM10 throughout the Winter season of 2020.,Vrindavan 6789,8486,spatio_temporal_aggregation,Which state had the 3rd highest median PM10 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""state""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Identify the state that showed the third highest median PM10 during the Winter season of 2024.,Himachal Pradesh 6790,8487,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Monsoon season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Report which station possessed the third most minimal median PM2.5 throughout the Monsoon season of 2021.,"Diwator Nagar, Koppal - KSPCB" 6791,8488,spatio_temporal_aggregation,Which city had the 2nd highest 75th percentile of PM10 during the Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Determine the city exhibiting the 2nd highest 75th percentile of PM10 over the Monsoon season of 2020.,Virudhunagar 6792,8489,spatio_temporal_aggregation,Which station had the 3rd lowest median PM2.5 during the Monsoon season of 2022?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Which station experienced the 3rd lowest median for PM2.5 in the Monsoon season of 2022?,"DM College of Science, Imphal - Manipur PCB" 6793,8491,spatio_temporal_aggregation,Which station had the lowest median PM10 during the Winter season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Report which station registered the most minimal median PM10 throughout the Winter season of 2018.,"Sanegurava Halli, Bengaluru - KSPCB" 6794,8492,spatio_temporal_aggregation,Which station had the 2nd lowest average PM10 during the Post-Monsoon season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([9, 10, 11])] data = data.groupby([""station""])[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Determine the station that showed the 2nd lowest average PM10 over the Post-Monsoon season of 2020.,"Lumpyngngad, Shillong - Meghalaya PCB" 6795,8493,spatio_temporal_aggregation,Which state had the highest median PM2.5 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""state""])[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Which state possessed the highest median for PM2.5 in the Monsoon season of 2018?,Uttarakhand 6796,8494,spatio_temporal_aggregation,Which station had the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2019?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""station""])[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Identify the station exhibiting the 2nd highest 25th percentile of PM2.5 during the Monsoon season of 2019.,"Yerramukkapalli, Kadapa - APPCB" 6797,8495,spatio_temporal_aggregation,Which city had the 3rd lowest 25th percentile of PM10 during the Monsoon season of 2018?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['Timestamp'].dt.month.isin([6, 7, 8])] data = data.groupby([""city""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Report which city experienced the third lowest 25th percentile of PM10 throughout the Monsoon season of 2018.,Pune 6798,8496,spatio_temporal_aggregation,Which station had the 2nd highest 75th percentile of PM2.5 during the Summer season of 2021?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the 2nd highest 75th percentile of PM2.5 over the Summer season of 2021.,"Yerramukkapalli, Kadapa - APPCB" 6799,8497,spatio_temporal_aggregation,Which station had the lowest 75th percentile of PM10 during the Summer season of 2023?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['Timestamp'].dt.month.isin([3, 4, 5])] data = data.groupby([""station""])[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Which station showed the lowest 75th percentile for PM10 in the Summer season of 2023?,"Brahmagiri, Udupi - KSPCB" 6800,8499,spatio_temporal_aggregation,Which station had the 2nd lowest 25th percentile of PM10 during the Winter season of 2020?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station possessed the 2nd most minimal 25th percentile of PM10 throughout the Winter season of 2020.,"Lumpyngngad, Shillong - Meghalaya PCB" 6801,8500,spatio_temporal_aggregation,Which station had the 3rd lowest average PM2.5 during the Winter season of 2024?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data[data['Timestamp'].dt.month.isin([12, 1, 2])] data = data.groupby([""station""])[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""station""]) true_code() ",Determine the station exhibiting the 3rd lowest average PM2.5 over the Winter season of 2024.,"Municipal Corporation Office, Tirunelveli - TNPCB" 6802,8501,spatio_temporal_aggregation,Which station recorded the 2nd highest median PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Identify the station that registered the second highest median PM10 level overall.,"Mundka, Delhi - DPCC" 6803,8502,spatio_temporal_aggregation,Which state recorded the 2nd highest 75th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state documented the second highest 75th percentile for PM2.5 historically.,Bihar 6804,8504,spatio_temporal_aggregation,Which station recorded the highest median PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the maximum median PM2.5 level ever recorded?,"New Moti Bagh, Delhi - MHUA" 6805,8505,spatio_temporal_aggregation,Which city recorded the lowest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""city""]) true_code() ",Identify the city that registered the minimum average PM2.5 level historically.,Aizawl 6806,8506,spatio_temporal_aggregation,Which state recorded the lowest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""state""]) true_code() ",Report which state documented the lowest average PM10 level of all time.,Sikkim 6807,8507,spatio_temporal_aggregation,Which station recorded the 3rd highest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Determine the station that recorded the third highest average PM10 level ever.,"Wazirpur, Delhi - DPCC" 6808,8508,spatio_temporal_aggregation,Which city recorded the 2nd highest median PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the second highest median PM2.5 level historically?,Bhiwadi 6809,8509,spatio_temporal_aggregation,Which city recorded the 2nd highest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Identify the city that registered the second highest 25th percentile for PM2.5 across all time.,Bhiwadi 6810,8510,spatio_temporal_aggregation,Which state recorded the 2nd highest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state documented the second highest 25th percentile of PM2.5 historically.,Himachal Pradesh 6811,8511,spatio_temporal_aggregation,Which city recorded the highest 75th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""city""]) true_code() ",Determine the city that recorded the maximum 75th percentile for PM2.5 ever.,Byrnihat 6812,8513,spatio_temporal_aggregation,Which city recorded the 3rd lowest 25th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Identify the city that registered the third lowest 25th percentile for PM10 across all time.,Shillong 6813,8514,spatio_temporal_aggregation,Which station recorded the 3rd highest median PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""station""]) true_code() ",Report which station documented the third highest median PM10 level ever.,"Jahangirpuri, Delhi - DPCC" 6814,8515,spatio_temporal_aggregation,Which station recorded the highest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""station""]) true_code() ",Determine the station that recorded the maximum average PM10 level historically.,"Anand Vihar, Delhi - DPCC" 6815,8517,spatio_temporal_aggregation,Which state recorded the 2nd lowest 75th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second most minimal 75th percentile for PM2.5 ever.,Sikkim 6816,8518,spatio_temporal_aggregation,Which city recorded the 2nd highest median PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""city""]) true_code() ",Report which city documented the second highest median PM10 level historically.,Sri Ganganagar 6817,8520,spatio_temporal_aggregation,Which city recorded the 2nd lowest median PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""city""]) true_code() ",Which city showed the second lowest median PM2.5 level ever recorded?,Gangtok 6818,8521,spatio_temporal_aggregation,Which state recorded the lowest 75th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Identify the state that registered the minimum 75th percentile for PM2.5 historically.,Mizoram 6819,8522,spatio_temporal_aggregation,Which station recorded the 2nd highest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Report which station documented the second highest 75th percentile of PM10 of all time.,"Mundka, Delhi - DPCC" 6820,8523,spatio_temporal_aggregation,Which station recorded the lowest 25th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Determine the station that recorded the minimum 25th percentile for PM10 ever.,"Zero Point GICI, Gangtok - SSPCB" 6821,8526,spatio_temporal_aggregation,Which state recorded the 3rd lowest median PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Report which state documented the third lowest median PM10 level ever.,Mizoram 6822,8528,spatio_temporal_aggregation,Which state recorded the highest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""state""]) true_code() ",Which state showed the maximum average PM10 level of all time?,Delhi 6823,8529,spatio_temporal_aggregation,Which state recorded the 2nd lowest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second most minimal 25th percentile for PM2.5 ever.,Sikkim 6824,8530,spatio_temporal_aggregation,Which station recorded the 2nd lowest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Report which station documented the second lowest average PM10 level historically.,"Semmandalam, Cuddalore - TNPCB" 6825,8531,spatio_temporal_aggregation,Which state recorded the 2nd lowest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Determine the state that recorded the second most minimal 75th percentile for PM10 across all time.,Meghalaya 6826,8532,spatio_temporal_aggregation,Which station recorded the highest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Which station showed the maximum 25th percentile of PM2.5 level ever recorded?,"New Moti Bagh, Delhi - MHUA" 6827,8534,spatio_temporal_aggregation,Which state recorded the 2nd highest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""state""]) true_code() ",Report which state documented the second highest average PM2.5 of all time.,Bihar 6828,8535,spatio_temporal_aggregation,Which state recorded the lowest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""state""]) true_code() ",Determine the state that recorded the minimum 25th percentile for PM2.5 ever.,Mizoram 6829,8536,spatio_temporal_aggregation,Which city recorded the 3rd lowest median PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Which city showed the third lowest median PM10 level historically?,Tirunelveli 6830,8537,spatio_temporal_aggregation,Which station recorded the lowest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""station""]) true_code() ",Identify the station that registered the minimum 75th percentile for PM10 across all time.,"Municipal Corporation Office, Tirunelveli - TNPCB" 6831,8538,spatio_temporal_aggregation,Which state recorded the 3rd lowest 25th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state documented the third lowest 25th percentile of PM2.5 ever.,Manipur 6832,8539,spatio_temporal_aggregation,Which station recorded the 2nd highest 25th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""station""]) true_code() ",Determine the station that recorded the second highest 25th percentile for PM10 historically.,"Central Academy for SFS, Byrnihat - PCBA" 6833,8540,spatio_temporal_aggregation,Which station recorded the 2nd lowest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""station""]) true_code() ",Which station showed the second most minimal 75th percentile of PM10 of all time?,"Semmandalam, Cuddalore - TNPCB" 6834,8541,spatio_temporal_aggregation,Which station recorded the highest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""station"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""station""]) true_code() ",Identify the station that registered the maximum average PM2.5 ever.,"New Moti Bagh, Delhi - MHUA" 6835,8542,spatio_temporal_aggregation,Which city recorded the lowest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""city""]) true_code() ",Report which city documented the lowest average PM10 level historically.,Tirunelveli 6836,8543,spatio_temporal_aggregation,Which state recorded the highest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""state""]) true_code() ",Determine the state that recorded the maximum average PM2.5 across all time.,Delhi 6837,8544,spatio_temporal_aggregation,Which city recorded the 3rd highest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Which city showed the third highest average PM10 level ever recorded?,Greater Noida 6838,8545,spatio_temporal_aggregation,Which state recorded the 2nd lowest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second most minimal average PM10 historically.,Meghalaya 6839,8546,spatio_temporal_aggregation,Which state recorded the 3rd lowest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""state""]) true_code() ",Report which state documented the third lowest average PM2.5 of all time.,Arunachal Pradesh 6840,8547,spatio_temporal_aggregation,Which city recorded the 3rd highest 25th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the third highest 25th percentile for PM10 ever.,Bhiwadi 6841,8548,spatio_temporal_aggregation,Which state recorded the 2nd lowest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Which state showed the second lowest average PM2.5 level historically?,Sikkim 6842,8549,spatio_temporal_aggregation,Which state recorded the 2nd lowest median PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""state""]) true_code() ",Identify the state that registered the second most minimal median PM2.5 across all time.,Sikkim 6843,8552,spatio_temporal_aggregation,Which city recorded the 2nd highest 75th percentile of PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""city""]) true_code() ",Which city showed the second highest 75th percentile of PM2.5 of all time?,Begusarai 6844,8553,spatio_temporal_aggregation,Which state recorded the 3rd lowest average PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""state""]) true_code() ",Identify the state that registered the third lowest average PM10 ever.,Mizoram 6845,8555,spatio_temporal_aggregation,Which city recorded the 3rd lowest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""city""]) true_code() ",Determine the city that recorded the third most minimal 75th percentile of PM10 across all time.,Gangtok 6846,8556,spatio_temporal_aggregation,Which state recorded the 3rd highest 75th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""state""]) true_code() ",Which state showed the third highest 75th percentile of PM10 level ever recorded?,Uttar Pradesh 6847,8557,spatio_temporal_aggregation,Which state recorded the 2nd highest 25th percentile of PM10 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""state"")[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""state""]) true_code() ",Identify the state that registered the second highest 25th percentile for PM10 historically.,Himachal Pradesh 6848,8559,spatio_temporal_aggregation,Which city recorded the 3rd highest average PM2.5 level ever?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(""city"")[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""city""]) true_code() ",Determine the city that recorded the third highest average PM2.5 ever.,Delhi 6849,8560,specific_pattern,Which date in the last five years recorded the lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","In Jaipur, which date during the last five years had the lowest recorded PM2.5 level?",2022-08-17 6850,8561,specific_pattern,Which date in the last two years recorded the 3rd lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past two years in Delhi, on which date was the PM2.5 level the third lowest?",2023-08-06 6851,8562,specific_pattern,Which date in the last five years recorded the 2nd lowest PM10 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","For Ahmedabad, what date in the last five years registered the second-lowest PM10 reading?",2023-07-08 6852,8563,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","In Mumbai, which date in the previous three years showed the third-lowest PM10 concentration?",2023-08-06 6853,8564,specific_pattern,Which date in the last five years recorded the 2nd highest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","Within the last five years in Jaipur, on what date was the PM2.5 level the second highest?",2023-07-18 6854,8565,specific_pattern,Which date in the last two years recorded the 2nd lowest PM2.5 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ","During the past two years in Kolkata, on which date was the PM2.5 level the second lowest?",2024-05-27 6855,8567,specific_pattern,Which date in the last three years recorded the 2nd highest PM2.5 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","In Ahmedabad, which date in the previous three years registered the second-highest PM2.5 concentration?",2022-02-01 6856,8568,specific_pattern,Which date in the last two years recorded the highest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","Within the last two years in Chennai, on what date was the PM2.5 level the highest?",2023-11-12 6857,8571,specific_pattern,Which date in the last three years recorded the 3rd highest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous three years had the third-highest PM10 concentration?",2024-11-18 6858,8572,specific_pattern,Which date in the last two years recorded the highest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","Within the last two years in Chennai, on what date was the PM10 level the highest?",2023-04-04 6859,8573,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past three years in Surat, on which date was the PM10 level the lowest?",2023-01-30 6860,8574,specific_pattern,Which date in the last two years recorded the 2nd lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","For Mumbai, what date in the last two years registered the second-lowest PM10 reading?",2023-08-05 6861,8577,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past five years in Delhi, on which date was the PM2.5 level the third lowest?",2023-08-06 6862,8580,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Within the last three years in Kolkata, on what date was the PM10 level the lowest?",2024-06-27 6863,8581,specific_pattern,Which date in the last four years recorded the 2nd lowest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ","Over the past four years in Chennai, on which date was the PM2.5 level the second lowest?",2022-07-08 6864,8582,specific_pattern,Which date in the last three years recorded the lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","For Jaipur, what date in the last three years showed the lowest PM2.5 reading?",2022-08-17 6865,8583,specific_pattern,Which date in the last two years recorded the highest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous two years had the highest PM10 concentration?",2023-01-07 6866,8586,specific_pattern,Which date in the last four years recorded the 2nd highest PM10 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","For Ahmedabad, what date in the last four years registered the second-highest PM10 reading?",2021-06-12 6867,8587,specific_pattern,Which date in the last two years recorded the highest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","In Jaipur, which date in the previous two years showed the highest PM10 concentration?",2023-07-18 6868,8588,specific_pattern,Which date in the last three years recorded the 2nd highest PM2.5 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","Within the last three years in Kolkata, on what date was the PM2.5 level the second highest?",2022-01-20 6869,8589,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past three years in Surat, on which date was the PM10 level the third lowest?",2023-02-01 6870,8591,specific_pattern,Which date in the last two years recorded the 2nd highest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","In Jaipur, which date in the previous two years registered the second-highest PM10 concentration?",2024-06-07 6871,8592,specific_pattern,Which date in the last five years recorded the 3rd highest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Within the last five years in Surat, on what date was the PM2.5 level the third highest?",2023-09-26 6872,8594,specific_pattern,Which date in the last two years recorded the lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","For Jaipur, what date in the last two years showed the lowest PM2.5 reading?",2023-09-09 6873,8595,specific_pattern,Which date in the last four years recorded the 3rd lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous four years had the third-lowest PM10 concentration?",2024-11-17 6874,8596,specific_pattern,Which date in the last three years recorded the 2nd lowest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","Within the last three years in Pune, on what date was the PM10 level the second lowest?",2022-07-03 6875,8597,specific_pattern,Which date in the last five years recorded the lowest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past five years in Hyderabad, on which date was the PM10 level the lowest?",2022-08-08 6876,8599,specific_pattern,Which date in the last five years recorded the lowest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","In Jaipur, which date in the previous five years showed the lowest PM10 concentration?",2022-08-17 6877,8600,specific_pattern,Which date in the last five years recorded the 3rd highest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Within the last five years in Pune, on what date was the PM2.5 level the third highest?",2022-08-18 6878,8601,specific_pattern,Which date in the last four years recorded the 2nd highest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","Over the past four years in Surat, on which date was the PM2.5 level the second highest?",2024-05-23 6879,8602,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","For Delhi, what date in the last three years had the third-lowest PM10 reading?",2024-11-17 6880,8603,specific_pattern,Which date in the last four years recorded the 3rd lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous four years registered the third-lowest PM2.5 concentration?",2023-09-18 6881,8604,specific_pattern,Which date in the last four years recorded the 2nd lowest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ","Within the last four years in Mumbai, on what date was the PM2.5 level the second lowest?",2024-05-20 6882,8606,specific_pattern,Which date in the last two years recorded the 2nd highest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","For Surat, what date in the last two years showed the second-highest PM2.5 reading?",2024-05-23 6883,8607,specific_pattern,Which date in the last five years recorded the 3rd lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous five years had the third-lowest PM10 concentration?",2024-11-17 6884,8608,specific_pattern,Which date in the last three years recorded the highest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","Within the last three years in Kolkata, on what date was the PM10 level the highest?",2022-12-13 6885,8609,specific_pattern,Which date in the last two years recorded the lowest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past two years in Pune, on which date was the PM2.5 level the lowest?",2023-06-11 6886,8610,specific_pattern,Which date in the last two years recorded the 2nd highest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","For Mumbai, what date in the last two years registered the second-highest PM10 reading?",2023-07-25 6887,8611,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","In Chennai, which date in the previous three years showed the lowest PM10 concentration?",2023-11-23 6888,8612,specific_pattern,Which date in the last five years recorded the 3rd highest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Within the last five years in Pune, on what date was the PM10 level the third highest?",2024-03-05 6889,8613,specific_pattern,Which date in the last five years recorded the highest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","Over the past five years in Mumbai, on which date was the PM2.5 level the highest?",2022-07-01 6890,8614,specific_pattern,Which date in the last two years recorded the 3rd highest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","For Mumbai, what date in the last two years had the third-highest PM2.5 reading?",2023-07-16 6891,8615,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","In Kolkata, which date in the previous four years registered the second-lowest PM10 concentration?",2024-06-27 6892,8616,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Within the last four years in Kolkata, on what date was the PM10 level the lowest?",2021-12-06 6893,8617,specific_pattern,Which date in the last five years recorded the 2nd highest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","Over the past five years in Delhi, on which date was the PM2.5 level the second highest?",2023-12-09 6894,8618,specific_pattern,Which date in the last three years recorded the 2nd highest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","For Jaipur, what date in the last three years showed the second-highest PM2.5 reading?",2023-07-18 6895,8619,specific_pattern,Which date in the last three years recorded the 2nd lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous three years had the second-lowest PM2.5 concentration?",2024-08-19 6896,8621,specific_pattern,Which date in the last five years recorded the lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past five years in Kolkata, on which date was the PM10 level the lowest?",2020-05-21 6897,8622,specific_pattern,Which date in the last two years recorded the 3rd lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","For Delhi, what date in the last two years registered the third-lowest PM10 reading?",2023-09-10 6898,8623,specific_pattern,Which date in the last three years recorded the lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","In Delhi, which date in the previous three years showed the lowest PM2.5 concentration?",2024-08-18 6899,8624,specific_pattern,Which date in the last two years recorded the 3rd lowest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Within the last two years in Pune, on what date was the PM10 level the third lowest?",2024-07-20 6900,8625,specific_pattern,Which date in the last four years recorded the 3rd lowest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past four years in Mumbai, on which date was the PM2.5 level the third lowest?",2024-06-25 6901,8629,specific_pattern,Which date in the last three years recorded the lowest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past three years in Mumbai, on which date was the PM2.5 level the lowest?",2023-08-14 6902,8630,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","For Surat, what date in the last four years showed the lowest PM10 reading?",2023-01-30 6903,8631,specific_pattern,Which date in the last five years recorded the 2nd lowest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","In Jaipur, which date in the previous five years had the second-lowest PM10 concentration?",2022-07-10 6904,8632,specific_pattern,Which date in the last three years recorded the highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","Within the last three years in Hyderabad, on what date was the PM10 level the highest?",2023-11-12 6905,8633,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past five years in Ahmedabad, on which date was the PM2.5 level the third lowest?",2023-09-25 6906,8634,specific_pattern,Which date in the last two years recorded the 2nd lowest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","For Jaipur, what date in the last two years registered the second-lowest PM10 reading?",2023-09-17 6907,8635,specific_pattern,Which date in the last three years recorded the highest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","In Pune, which date in the previous three years showed the highest PM10 concentration?",2022-08-23 6908,8636,specific_pattern,Which date in the last three years recorded the 3rd highest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Within the last three years in Mumbai, on what date was the PM10 level the third highest?",2022-08-14 6909,8637,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past three years in Mumbai, on which date was the PM10 level the lowest?",2024-05-20 6910,8638,specific_pattern,Which date in the last five years recorded the 3rd lowest PM10 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","For Ahmedabad, what date in the last five years had the third-lowest PM10 reading?",2023-07-09 6911,8639,specific_pattern,Which date in the last four years recorded the 3rd highest PM2.5 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","In Hyderabad, which date in the previous four years registered the third-highest PM2.5 concentration?",2024-01-04 6912,8640,specific_pattern,Which date in the last three years recorded the lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Within the last three years in Delhi, on what date was the PM10 level the lowest?",2022-09-20 6913,8641,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Over the past four years in Pune, on which date was the PM10 level the lowest?",2021-06-09 6914,8643,specific_pattern,Which date in the last five years recorded the 3rd lowest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","In Hyderabad, which date in the previous five years had the third-lowest PM10 concentration?",2021-07-22 6915,8644,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Within the last five years in Mumbai, on what date was the PM2.5 level the third lowest?",2020-08-06 6916,8645,specific_pattern,Which date in the last four years recorded the 3rd highest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Over the past four years in Mumbai, on which date was the PM2.5 level the third highest?",2023-07-19 6917,8647,specific_pattern,Which date in the last five years recorded the 2nd lowest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","In Hyderabad, which date in the previous five years showed the second-lowest PM10 concentration?",2022-09-12 6918,8648,specific_pattern,Which date in the last two years recorded the 2nd lowest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","Within the last two years in Chennai, on what date was the PM10 level the second lowest?",2023-11-06 6919,8649,specific_pattern,Which date in the last two years recorded the 3rd lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ","Over the past two years in Mumbai, on which date was the PM10 level the third lowest?",2023-08-06 6920,8650,specific_pattern,Which date in the last five years recorded the 2nd lowest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","For Delhi, what date in the last five years had the second-lowest PM10 reading?",2022-09-20 6921,8651,specific_pattern,Which date in the last five years recorded the 2nd highest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ","In Surat, which date in the previous five years registered the second-highest PM2.5 concentration?",2024-05-23 6922,8652,specific_pattern,Which date in the last two years recorded the lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ","Within the last two years in Kolkata, on what date was the PM10 level the lowest?",2024-06-27 6923,8653,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ","Over the past four years in Chennai, on which date was the PM10 level the second lowest?",2023-11-23 6924,8654,specific_pattern,Which date in the last three years recorded the 2nd lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ","For Jaipur, what date in the last three years showed the second-lowest PM2.5 reading?",2022-08-16 6925,8655,specific_pattern,Which date in the last three years recorded the lowest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ","In Pune, which date in the previous three years had the lowest PM2.5 concentration?",2023-06-11 6926,8657,specific_pattern,Which date in the last four years recorded the 3rd highest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ","Over the past four years in Kolkata, on which date was the PM10 level the third highest?",2021-01-18 6927,8658,specific_pattern,Which date in the last four years recorded the highest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ","For Mumbai, what date in the last four years registered the highest PM10 reading?",2022-05-03 6928,8659,specific_pattern,Which date in the last four years recorded the lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""].date()) true_code() ",On which date in the past four years did Mumbai record its minimum PM10 level?,2024-05-20 6929,8660,specific_pattern,Which date in the last four years recorded the highest PM2.5 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",On which date in the previous four years did Ahmedabad register its peak PM2.5 level?,2021-09-16 6930,8662,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date over the last four years did Pune note the 2nd minimum PM10 level?,2022-07-04 6931,8663,specific_pattern,Which date in the last five years recorded the 2nd lowest PM2.5 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ",What date within the previous five years showed Kolkata's 2nd lowest PM2.5 reading?,2020-08-06 6932,8664,specific_pattern,Which date in the last five years recorded the 3rd highest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the past five years did Mumbai record its 3rd highest PM2.5 level?,2023-07-22 6933,8665,specific_pattern,Which date in the last five years recorded the 3rd highest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date during the last five years noted Jaipur's 3rd maximum PM2.5 reading?,2024-06-07 6934,8666,specific_pattern,Which date in the last two years recorded the 2nd lowest PM2.5 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date in the previous two years did Hyderabad register its 2nd minimum PM2.5 level?,2023-11-09 6935,8667,specific_pattern,Which date in the last four years recorded the 3rd lowest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date over the last four years showed Pune's 3rd lowest PM2.5 reading?,2022-07-04 6936,8668,specific_pattern,Which date in the last two years recorded the highest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",On which date in the past two years did Mumbai record its peak PM2.5 level?,2024-05-19 6937,8670,specific_pattern,Which date in the last four years recorded the lowest PM2.5 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""].date()) true_code() ",On which date in the previous four years did Hyderabad register its minimum PM2.5 level?,2022-09-16 6938,8672,specific_pattern,Which date in the last four years recorded the 3rd highest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the last four years did Chennai record its 3rd highest PM2.5 level?,2022-07-11 6939,8673,specific_pattern,Which date in the last four years recorded the 3rd highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date over the previous four years noted Hyderabad's 3rd maximum PM10 reading?,2023-11-12 6940,8674,specific_pattern,Which date in the last two years recorded the 2nd highest PM10 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the past two years did Delhi register its 2nd peak PM10 level?,2024-11-18 6941,8676,specific_pattern,Which date in the last three years recorded the 2nd lowest PM2.5 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date in the previous three years did Kolkata record its 2nd minimum PM2.5 level?,2022-07-10 6942,8678,specific_pattern,Which date in the last four years recorded the 3rd lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ",On which date in the past four years did Mumbai register its 3rd minimum PM10 level?,2023-08-06 6943,8679,specific_pattern,Which date in the last four years recorded the 2nd lowest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ",What date during the previous four years showed Pune's 2nd lowest PM2.5 reading?,2024-09-28 6944,8680,specific_pattern,Which date in the last three years recorded the 2nd highest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the last three years did Pune record its 2nd highest PM2.5 level?,2023-05-25 6945,8681,specific_pattern,Which date in the last three years recorded the 3rd highest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date over the previous three years noted Surat's 3rd maximum PM10 reading?,2024-11-22 6946,8682,specific_pattern,Which date in the last three years recorded the 3rd lowest PM2.5 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",On which date in the past three years did Mumbai register its 3rd minimum PM2.5 level?,2024-06-25 6947,8683,specific_pattern,Which date in the last two years recorded the highest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date during the last two years showed Mumbai's peak PM10 reading?,2023-07-08 6948,8686,specific_pattern,Which date in the last five years recorded the 2nd highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the past five years did Hyderabad register its 2nd peak PM10 level?,2024-11-02 6949,8687,specific_pattern,Which date in the last three years recorded the highest PM2.5 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date during the previous three years showed Hyderabad's highest PM2.5 reading?,2024-04-28 6950,8688,specific_pattern,Which date in the last two years recorded the 3rd highest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the last two years did Chennai record its 3rd highest PM2.5 level?,2024-10-31 6951,8689,specific_pattern,Which date in the last five years recorded the 3rd highest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date over the past five years noted Jaipur's 3rd maximum PM10 reading?,2023-07-18 6952,8690,specific_pattern,Which date in the last three years recorded the 3rd highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the previous three years did Hyderabad register its 3rd peak PM10 level?,2023-11-12 6953,8691,specific_pattern,Which date in the last three years recorded the 3rd highest PM2.5 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date during the last three years showed Hyderabad's 3rd highest PM2.5 reading?,2024-01-04 6954,8693,specific_pattern,Which date in the last three years recorded the 3rd highest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date over the previous three years noted Kolkata's 3rd highest PM10 reading?,2022-12-23 6955,8694,specific_pattern,Which date in the last five years recorded the 2nd lowest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date in the last five years did Surat register its 2nd minimum PM2.5 level?,2023-04-18 6956,8695,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ",What date during the past four years showed Jaipur's 2nd lowest PM10 reading?,2022-07-10 6957,8696,specific_pattern,Which date in the last two years recorded the 2nd highest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the previous two years did Pune record its 2nd peak PM2.5 level?,2023-05-25 6958,8697,specific_pattern,Which date in the last three years recorded the highest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date over the last three years noted Chennai's highest PM2.5 reading?,2022-07-11 6959,8698,specific_pattern,Which date in the last four years recorded the 3rd lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",On which date in the past four years did Jaipur register its 3rd minimum PM2.5 level?,2023-09-09 6960,8699,specific_pattern,Which date in the last four years recorded the 3rd lowest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date during the previous four years showed Surat's 3rd lowest PM10 reading?,2023-02-01 6961,8701,specific_pattern,Which date in the last two years recorded the 2nd highest PM2.5 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",What date over the past two years noted Ahmedabad's 2nd maximum PM2.5 reading?,2024-02-27 6962,8704,specific_pattern,Which date in the last five years recorded the 2nd highest PM2.5 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the past five years did Kolkata record its 2nd peak PM2.5 level?,2021-01-18 6963,8705,specific_pattern,Which date in the last three years recorded the highest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date over the previous three years noted Chennai's highest PM10 reading?,2022-01-15 6964,8706,specific_pattern,Which date in the last four years recorded the 3rd highest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the last four years did Pune register its 3rd maximum PM10 level?,2024-03-05 6965,8707,specific_pattern,Which date in the last three years recorded the 3rd lowest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date during the past three years showed Chennai's 3rd lowest PM10 reading?,2022-11-20 6966,8709,specific_pattern,Which date in the last four years recorded the highest PM10 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date over the last four years noted Pune's highest PM10 reading?,2022-08-23 6967,8710,specific_pattern,Which date in the last three years recorded the 2nd lowest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date in the past three years did Kolkata register its 2nd minimum PM10 level?,2023-06-27 6968,8711,specific_pattern,Which date in the last three years recorded the 3rd lowest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date during the previous three years showed Delhi's 3rd lowest PM2.5 reading?,2023-08-05 6969,8712,specific_pattern,Which date in the last five years recorded the 3rd highest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the last five years did Chennai record its 3rd peak PM2.5 level?,2021-05-22 6970,8713,specific_pattern,Which date in the last four years recorded the 3rd highest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",What date over the past four years noted Jaipur's 3rd maximum PM2.5 reading?,2024-06-07 6971,8714,specific_pattern,Which date in the last two years recorded the 3rd highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the previous two years did Hyderabad register its 3rd peak PM10 level?,2023-11-12 6972,8715,specific_pattern,Which date in the last four years recorded the 2nd highest PM10 in the Kolkata ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Kolkata"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",What date during the last four years showed Kolkata's 2nd highest PM10 reading?,2021-01-07 6973,8717,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Pune ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Pune"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date over the previous five years noted Pune's 3rd lowest PM2.5 reading?,2022-07-04 6974,8718,specific_pattern,Which date in the last three years recorded the highest PM10 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",On which date in the last three years did Jaipur register its peak PM10 level?,2022-05-22 6975,8720,specific_pattern,Which date in the last five years recorded the 3rd lowest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",On which date in the previous five years did Jaipur record its 3rd minimum PM2.5 level?,2023-09-09 6976,8721,specific_pattern,Which date in the last two years recorded the 3rd lowest PM10 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date over the last two years noted Ahmedabad's 3rd lowest PM10 reading?,2023-07-09 6977,8723,specific_pattern,Which date in the last three years recorded the 3rd lowest PM2.5 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""].date()) true_code() ",What date during the previous three years showed Chennai's 3rd lowest PM2.5 reading?,2023-11-06 6978,8724,specific_pattern,Which date in the last five years recorded the 2nd lowest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ",On which date in the last five years did Mumbai record its 2nd minimum PM10 level?,2023-08-05 6979,8726,specific_pattern,Which date in the last five years recorded the 3rd lowest PM10 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2][""Timestamp""].date()) true_code() ",On which date in the previous five years did Surat register its 3rd minimum PM10 level?,2023-02-01 6980,8727,specific_pattern,Which date in the last four years recorded the 2nd lowest PM10 in the Ahmedabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Ahmedabad"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""].date()) true_code() ",What date during the last four years showed Ahmedabad's 2nd lowest PM10 reading?,2023-07-08 6981,8728,specific_pattern,Which date in the last three years recorded the 3rd highest PM2.5 in the Jaipur ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Jaipur"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""].date()) true_code() ",On which date in the past three years did Jaipur record its 3rd peak PM2.5 level?,2024-06-07 6982,8729,specific_pattern,Which date in the last four years recorded the highest PM2.5 in the Surat ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Surat"") & (main_data['Timestamp'].dt.year >= (year - 4))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",What date over the previous four years noted Surat's highest PM2.5 reading?,2023-12-04 6983,8730,specific_pattern,Which date in the last two years recorded the 2nd highest PM2.5 in the Delhi ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Delhi"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",On which date in the last two years did Delhi register its 2nd peak PM2.5 level?,2023-12-09 6984,8731,specific_pattern,Which date in the last two years recorded the 2nd highest PM10 in the Hyderabad ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Hyderabad"") & (main_data['Timestamp'].dt.year >= (year - 2))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",What date during the past two years showed Hyderabad's 2nd highest PM10 reading?,2024-11-02 6985,8733,specific_pattern,Which date in the last five years recorded the 2nd highest PM10 in the Chennai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Chennai"") & (main_data['Timestamp'].dt.year >= (year - 5))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""].date()) true_code() ",What date over the last five years noted Chennai's 2nd maximum PM10 reading?,2021-05-14 6986,8734,specific_pattern,Which date in the last three years recorded the highest PM10 in the Mumbai ?," def true_code(): import numpy as np import pandas as pd import datetime main_data = pd.read_pickle(""preprocessed/main_data.pkl"") year = datetime.datetime.now().year data = main_data[(main_data['city'] == ""Mumbai"") & (main_data['Timestamp'].dt.year >= (year - 3))] data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""].date()) true_code() ",On which date in the past three years did Mumbai register its peak PM10 level?,2022-05-03 6987,8736,specific_pattern,Which Indian station recorded the lowest PM10 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") val = data.iloc[0][""PM10""] data = data[data[""PM10""] == val] data = data.groupby(""station"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""station""]) true_code() ",Which Indian station registered the minimum PM10 levels for a single day in the previous decade?,"Talcher Coalfields,Talcher - OSPCB" 6988,8737,specific_pattern,Which Indian city recorded the highest PM10 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") val = data.iloc[-1][""PM10""] data = data[data[""PM10""] == val] data = data.groupby(""city"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""city""]) true_code() ",Which Indian city noted the maximum PM10 levels for a single day over the last decade?,Delhi 6989,8739,specific_pattern,Which Indian station recorded the 3rd lowest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[2][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""station"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""station""]) true_code() ",Which Indian station registered the 3rd minimum PM2.5 levels for a single day in the previous decade?,"BWSSB Kadabesanahalli, Bengaluru - CPCB" 6990,8740,specific_pattern,Which Indian city recorded the 3rd highest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[-3][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""city"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""city""]) true_code() ",Which Indian city noted the 3rd maximum PM2.5 levels for a single day over the last decade?,Brajrajnagar 6991,8741,specific_pattern,Which Indian station recorded the 3rd lowest PM10 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") val = data.iloc[2][""PM10""] data = data[data[""PM10""] == val] data = data.groupby(""station"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""station""]) true_code() ",Which Indian station recorded the 3rd lowest PM10 levels for a single day in the past decade?,"Ratanpura, Rupnagar - Ambuja Cements" 6992,8744,specific_pattern,Which Indian state recorded the 2nd lowest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[1][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""state"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""state""]) true_code() ",Which Indian state recorded the 2nd lowest PM2.5 levels for a single day in the past decade?,Karnataka 6993,8745,specific_pattern,Which Indian city recorded the 3rd highest PM10 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") val = data.iloc[-3][""PM10""] data = data[data[""PM10""] == val] data = data.groupby(""city"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""city""]) true_code() ",Which Indian city registered the 3rd maximum PM10 levels for a single day in the previous decade?,Delhi 6994,8747,specific_pattern,Which Indian station recorded the lowest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[0][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""station"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""station""]) true_code() ",Which Indian station recorded the minimum PM2.5 levels for a single day in the past decade?,"Alandur Bus Depot, Chennai - CPCB" 6995,8748,specific_pattern,Which Indian state recorded the highest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[-1][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""state"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""state""]) true_code() ",Which Indian state registered the maximum PM2.5 levels for a single day in the previous decade?,Odisha 6996,8749,specific_pattern,Which Indian state recorded the 3rd highest PM2.5 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") val = data.iloc[-3][""PM2.5""] data = data[data[""PM2.5""] == val] data = data.groupby(""state"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""state""]) true_code() ",Which Indian state noted the 3rd maximum PM2.5 levels for a single day over the last decade?,Odisha 6997,8750,specific_pattern,Which Indian station recorded the 3rd highest PM10 levels for single-day in the past decade?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") val = data.iloc[-3][""PM10""] data = data[data[""PM10""] == val] data = data.groupby(""station"").size().reset_index(name=""count"") data = data.sort_values(by=""count"") print(data.iloc[0][""station""]) true_code() ",Which Indian station recorded the 3rd highest PM10 levels for a single day in the past decade?,"DTU, Delhi - CPCB" 6998,8751,specific_pattern,"Which month (e.g. January, February,... etc) from 2018-2021 has consistently recorded India's worst air quality index (AQI)?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year >= 2018) & (main_data[""Timestamp""].dt.year <= 2021)] data = data.groupby(data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""]) true_code() ","Which month (e.g., January, February, etc.) from 2018-2021 has regularly shown India's poorest air quality index (AQI)?",November 6999,8752,specific_pattern,"Which month (e.g. January, February,... etc) from 2018-2024 has consistently recorded India's worst air quality index (AQI)?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year >= 2018) & (main_data[""Timestamp""].dt.year <= 2024)] data = data.groupby(data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""]) true_code() ","Which month (e.g., January, February, etc.) from 2018-2024 has steadily recorded India's worst air quality index (AQI)?",November 7000,8753,specific_pattern,"Which month (e.g. January, February,... etc) from 2018-2022 has consistently recorded India's worst air quality index (AQI)?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[(main_data[""Timestamp""].dt.year >= 2018) & (main_data[""Timestamp""].dt.year <= 2022)] data = data.groupby(data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""]) true_code() ","Which month (e.g., January, February, etc.) from 2018-2022 has consistently registered India's poorest air quality index (AQI)?",November 7001,8754,specific_pattern,Find a week with Surat's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Identify a week showing Surat's 3rd maximum PM10 levels across the specified years.,47.0 7002,8755,specific_pattern,Find a week with Delhi's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Determine a week with Delhi's 3rd highest PM10 levels over all these years.,45.0 7003,8757,specific_pattern,Find a week with Surat's lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Identify a week showing Surat's lowest PM10 levels across the specified years.,15.0 7004,8758,specific_pattern,Find a week with Kolkata's 2nd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ",Determine a week with Kolkata's 2nd highest PM2.5 levels over all these years.,3.0 7005,8760,specific_pattern,Find a week with Hyderabad's 3rd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Identify a week showing Hyderabad's 3rd maximum PM2.5 levels across the specified years.,52.0 7006,8761,specific_pattern,Find a week with Ahmedabad's 2nd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Determine a week with Ahmedabad's 2nd lowest PM10 levels over all these years.,36.0 7007,8762,specific_pattern,Find a week with Pune's lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Pune""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Find a week exhibiting Pune's minimum PM10 levels for all specified years.,30.0 7008,8763,specific_pattern,Find a week with Delhi's 3rd lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ",Identify a week showing Delhi's 3rd minimum PM2.5 levels across the specified years.,30.0 7009,8764,specific_pattern,Find a week with Surat's 2nd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Determine a week with Surat's 2nd lowest PM10 levels over all these years.,16.0 7010,8766,specific_pattern,Find a week with Mumbai's 2nd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ",Identify a week showing Mumbai's 2nd maximum PM2.5 levels across the specified years.,1.0 7011,8767,specific_pattern,Find a week with Surat's 2nd lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Determine a week with Surat's 2nd lowest PM2.5 levels over all these years.,16.0 7012,8768,specific_pattern,Find a week with Kolkata's 2nd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Identify a week showing Kolkata's 2nd minimum PM10 levels across the specified years.,29.0 7013,8769,specific_pattern,Find a week with Chennai's 2nd lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Determine a week with Chennai's 2nd lowest PM2.5 levels over all these years.,18.0 7014,8770,specific_pattern,Find a week with Surat's 3rd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Find a week exhibiting Surat's 3rd maximum PM2.5 levels for all specified years.,47.0 7015,8771,specific_pattern,Find a week with Jaipur's lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Identify a week showing Jaipur's minimum PM2.5 levels across the specified years.,36.0 7016,8772,specific_pattern,Find a week with Ahmedabad's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Determine a week with Ahmedabad's 3rd highest PM10 levels over all these years.,43.0 7017,8774,specific_pattern,Find a week with Hyderabad's lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Identify a week showing Hyderabad's minimum PM2.5 levels across the specified years.,29.0 7018,8775,specific_pattern,Find a week with Kolkata's 3rd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Determine a week with Kolkata's 3rd highest PM2.5 levels over all these years.,52.0 7019,8776,specific_pattern,Find a week with Hyderabad's highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ",Find a week exhibiting Hyderabad's maximum PM2.5 levels for all specified years.,53.0 7020,8777,specific_pattern,Find a week with Hyderabad's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Determine a week with Hyderabad's 3rd highest PM10 levels over all these years.,2.0 7021,8780,specific_pattern,Find a week with Delhi's lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Determine a week with Delhi's lowest PM10 levels over all these years.,32.0 7022,8781,specific_pattern,Find a week with Ahmedabad's 2nd lowest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Find a week exhibiting Ahmedabad's 2nd minimum PM2.5 levels for all specified years.,36.0 7023,8782,specific_pattern,Find a week with Pune's 3rd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Identify a week showing Pune's 3rd maximum PM2.5 levels across the specified years.,4.0 7024,8783,specific_pattern,Find a week with Pune's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Pune""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Determine a week with Pune's 3rd highest PM10 levels over all these years.,4.0 7025,8784,specific_pattern,Find a week with Delhi's 3rd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Find a week exhibiting Delhi's 3rd maximum PM2.5 levels for all specified years.,45.0 7026,8785,specific_pattern,Find a week with Hyderabad's 3rd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ",Identify a week showing Hyderabad's 3rd minimum PM10 levels across the specified years.,30.0 7027,8787,specific_pattern,Find a week with Jaipur's 3rd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ",Find a week exhibiting Jaipur's 3rd minimum PM10 levels for all specified years.,32.0 7028,8788,specific_pattern,Find a week with Hyderabad's 2nd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ",Identify a week showing Hyderabad's 2nd maximum PM2.5 levels across the specified years.,51.0 7029,8789,specific_pattern,Find a week with Ahmedabad's 2nd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ",Determine a week with Ahmedabad's 2nd highest PM10 levels over all these years.,44.0 7030,8790,specific_pattern,Find a week with Delhi's highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ",Find a week exhibiting Delhi's maximum PM2.5 levels for all specified years.,44.0 7031,8791,specific_pattern,Find a week with Chennai's 3rd highest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Identify a week showing Chennai's 3rd maximum PM10 levels across the specified years.,4.0 7032,8792,specific_pattern,Find a week with Jaipur's lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Determine a week with Jaipur's lowest PM10 levels over all these years.,36.0 7033,8793,specific_pattern,Find a week with Ahmedabad's 2nd highest PM2.5 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ",Find a week exhibiting Ahmedabad's 2nd maximum PM2.5 levels for all specified years.,44.0 7034,8794,specific_pattern,Find a week with Hyderabad's 2nd lowest PM10 levels for all these years," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Identify a week showing Hyderabad's 2nd minimum PM10 levels across the specified years.,28.0 7035,8796,specific_pattern,Identify a year in which Chennai experienced the cleanest air from 2018-2024," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Chennai""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Chennai experienced its best air quality from 2018-2024.,2021.0 7036,8797,specific_pattern,Identify a year in which Kolkata experienced the cleanest air from 2018-2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Kolkata""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2020)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Kolkata witnessed its cleanest air quality between 2018 and 2020.,2020.0 7037,8798,specific_pattern,Identify a year in which Pune experienced the cleanest air from 2018-2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Pune""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2023)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Pune experienced its best air quality from 2018-2023.,2021.0 7038,8799,specific_pattern,Identify a year in which Pune experienced the cleanest air from 2018-2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Pune""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Pune witnessed its cleanest air quality between 2018 and 2021.,2021.0 7039,8800,specific_pattern,Identify a year in which Jaipur experienced the cleanest air from 2018-2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Jaipur""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Jaipur experienced its best air quality from 2018-2021.,2020.0 7040,8801,specific_pattern,Identify a year in which Delhi experienced the cleanest air from 2018-2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Delhi witnessed its cleanest air quality between 2018 and 2021.,2020.0 7041,8803,specific_pattern,Identify a year in which Mumbai experienced the cleanest air from 2018-2024," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Mumbai""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Mumbai experienced its best air quality from 2018-2024.,2024.0 7042,8804,specific_pattern,Identify a year in which Delhi experienced the cleanest air from 2018-2024," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Delhi""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Delhi witnessed its cleanest air quality between 2018 and 2024.,2020.0 7043,8805,specific_pattern,Identify a year in which Kolkata experienced the cleanest air from 2018-2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Kolkata""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2022)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Kolkata experienced its best air quality from 2018-2022.,2020.0 7044,8806,specific_pattern,Identify a year in which Surat experienced the cleanest air from 2018-2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Surat""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2023)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Surat witnessed its cleanest air quality between 2018 and 2023.,2023.0 7045,8807,specific_pattern,Identify a year in which Ahmedabad experienced the cleanest air from 2018-2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2023)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Ahmedabad experienced its best air quality from 2018-2023.,2020.0 7046,8808,specific_pattern,Identify a year in which Ahmedabad experienced the cleanest air from 2018-2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Ahmedabad""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2021)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Ahmedabad witnessed its cleanest air quality between 2018 and 2021.,2020.0 7047,8809,specific_pattern,Identify a year in which Hyderabad experienced the cleanest air from 2018-2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2022)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Hyderabad experienced its best air quality from 2018-2022.,2022.0 7048,8810,specific_pattern,Identify a year in which Jaipur experienced the cleanest air from 2018-2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Jaipur""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2022)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Determine a year when Jaipur witnessed its cleanest air quality between 2018 and 2022.,2020.0 7049,8811,specific_pattern,Identify a year in which Hyderabad experienced the cleanest air from 2018-2024," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""city""] == ""Hyderabad""] data = data[(data[""Timestamp""].dt.year >= 2018) & (data[""Timestamp""].dt.year <= 2024)] data = data.dropna(subset=""PM2.5"") data = data.groupby(data[""Timestamp""].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify a year in which Hyderabad experienced its best air quality from 2018-2024.,2024.0 7050,8812,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on average in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2022, on which day of the week were average PM2.5 pollution levels the second highest?",Thursday 7051,8813,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on 25th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","For the year 2024, which weekday exhibited the second-highest 25th percentile of PM10 pollution levels?",Wednesday 7052,8814,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 25th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ",Identify the weekday in 2022 that experienced the minimum 25th percentile of PM2.5 pollution levels.,Sunday 7053,8815,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on average in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering 2024, which day of the week registered the second-highest PM10 pollution levels on average?",Saturday 7054,8816,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","In 2024, which weekday was associated with the second-lowest median PM10 pollution levels?",Thursday 7055,8817,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 25th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","During 2021, what weekday showed the maximum 25th percentile of PM10 pollution concentrations?",Friday 7056,8818,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on median in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","For 2019, determine the weekday with the second-lowest median PM2.5 pollution levels.",Saturday 7057,8819,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","In the year 2018, which weekday had the lowest 75th percentile of PM2.5 pollution levels?",Sunday 7058,8820,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 25th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ",Which weekday in 2023 recorded the minimum 25th percentile for PM2.5 pollution levels?,Sunday 7059,8821,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on average in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2018, which day of the week had the third-highest average PM10 pollution levels?",Monday 7060,8823,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","For the year 2024, identify the weekday with the highest median PM2.5 pollution levels.",Tuesday 7061,8824,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on median in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","In 2020, which weekday experienced the maximum median PM2.5 pollution levels?",Wednesday 7062,8825,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 25th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","During 2023, which weekday saw the second-highest 25th percentile of PM2.5 pollution levels?",Thursday 7063,8826,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on 25th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering 2020, what weekday displayed the second-highest 25th percentile for PM10 pollution levels?",Tuesday 7064,8827,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on 25th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","In 2024, which day of the week corresponded to the third-lowest 25th percentile of PM2.5 pollution levels?",Thursday 7065,8828,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on 25th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","For the year 2022, which weekday had the third-highest 25th percentile of PM10 pollution levels?",Monday 7066,8829,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ",Identify the weekday in 2021 that registered the third-highest median PM10 pollution levels.,Thursday 7067,8830,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on median in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2022, which weekday was associated with the second-highest median PM10 pollution concentrations?",Tuesday 7068,8831,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on median in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","During 2022, determine the weekday that showed the second-highest median PM2.5 pollution levels.",Thursday 7069,8832,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","For 2018, which weekday experienced the second-lowest 75th percentile of PM10 pollution levels?",Tuesday 7070,8833,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on average in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2023, what day of the week had the third-highest average PM2.5 pollution levels?",Friday 7071,8834,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 75th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","In the year 2022, which weekday recorded the second-highest 75th percentile for PM2.5 pollution levels?",Thursday 7072,8836,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on median in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","For 2018, identify the weekday with the second-highest median PM2.5 pollution levels.",Saturday 7073,8837,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on median in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","In 2019, which weekday experienced the maximum median PM2.5 pollution concentrations?",Wednesday 7074,8838,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on 75th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","During 2024, which weekday saw the third-highest 75th percentile of PM2.5 pollution levels?",Wednesday 7075,8839,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on median in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering 2020, what weekday displayed the second-lowest median PM10 pollution levels?",Friday 7076,8840,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","In 2018, which day of the week corresponded to the third-lowest 75th percentile of PM10 pollution levels?",Monday 7077,8841,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on median in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","For the year 2018, which weekday had the third-highest median PM2.5 pollution levels?",Friday 7078,8842,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 25th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ",Identify the weekday in 2022 that registered the highest 25th percentile of PM10 pollution levels.,Tuesday 7079,8844,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","During 2024, determine the weekday that showed the lowest median PM10 pollution levels.",Sunday 7080,8845,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","For 2018, which weekday experienced the highest 75th percentile of PM2.5 pollution levels?",Friday 7081,8846,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on average in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2019, what day of the week had the third-highest average PM10 pollution levels?",Tuesday 7082,8847,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on average in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","In the year 2024, which weekday recorded the third-lowest average PM10 pollution levels?",Friday 7083,8848,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on average in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ",Which weekday in 2020 was linked to the third-highest average PM10 pollution levels?,Monday 7084,8849,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","For 2021, identify the weekday with the second-lowest median PM10 pollution levels.",Sunday 7085,8850,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on 25th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2019, which weekday experienced the second-highest 25th percentile of PM10 pollution concentrations?",Monday 7086,8851,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","During 2018, which weekday saw the lowest 75th percentile of PM10 pollution levels?",Sunday 7087,8852,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 75th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering 2019, what weekday displayed the second-highest 75th percentile for PM2.5 pollution levels?",Wednesday 7088,8853,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on median in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","In 2019, which day of the week corresponded to the highest median PM10 pollution levels?",Wednesday 7089,8854,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on 25th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","For the year 2021, which weekday had the third-lowest 25th percentile of PM2.5 pollution levels?",Wednesday 7090,8857,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on average in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","During 2023, determine the weekday that showed the second-lowest average PM10 pollution levels.",Sunday 7091,8858,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 75th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","For 2023, which weekday experienced the lowest 75th percentile of PM2.5 pollution levels?",Monday 7092,8859,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on median in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering 2023, what day of the week had the second-highest median PM2.5 pollution levels?",Friday 7093,8860,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on average in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","In the year 2021, which weekday recorded the third-lowest average PM2.5 pollution levels?",Sunday 7094,8862,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on average in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","For 2019, identify the weekday with the third-highest average PM2.5 pollution levels.",Tuesday 7095,8864,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","During 2024, which weekday saw the third-highest median PM10 pollution levels?",Monday 7096,8865,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on 75th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Considering 2021, what weekday displayed the third-lowest 75th percentile for PM2.5 pollution levels?",Friday 7097,8867,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on average in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","For the year 2023, which weekday had the second-highest average PM2.5 pollution levels?",Saturday 7098,8869,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","In 2021, which weekday was associated with the third-lowest median PM2.5 pollution concentrations?",Monday 7099,8872,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 75th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering 2024, what day of the week had the highest 75th percentile of PM10 pollution levels?",Tuesday 7100,8873,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 25th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","In the year 2024, which weekday recorded the second-highest 25th percentile for PM2.5 pollution levels?",Wednesday 7101,8874,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ",Which weekday in 2024 was linked to the third-lowest median PM2.5 pollution levels?,Saturday 7102,8876,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 75th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","In 2021, which weekday experienced the minimum 75th percentile of PM2.5 pollution concentrations?",Saturday 7103,8877,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on average in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","During 2023, which weekday saw the lowest average PM10 pollution levels?",Monday 7104,8878,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on average in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering 2018, what weekday displayed the second-highest average PM10 pollution levels?",Friday 7105,8879,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on median in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","In 2023, which day of the week corresponded to the second-lowest median PM2.5 pollution levels?",Tuesday 7106,8880,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on average in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","For the year 2019, which weekday had the second-highest average PM2.5 pollution levels?",Wednesday 7107,8881,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ",Identify the weekday in 2018 that registered the second-lowest 75th percentile of PM2.5 pollution levels.,Monday 7108,8883,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on 25th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","During 2020, determine the weekday that showed the lowest 25th percentile of PM10 pollution levels.",Sunday 7109,8884,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on median in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","For 2022, which weekday experienced the highest median PM2.5 pollution levels?",Wednesday 7110,8885,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on average in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering 2024, what day of the week had the highest average PM2.5 pollution levels?",Tuesday 7111,8886,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","In the year 2024, which weekday recorded the third-highest median PM2.5 pollution levels?",Monday 7112,8887,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on average in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ",Which weekday in 2021 was linked to the third-highest average PM2.5 pollution levels?,Wednesday 7113,8889,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on 75th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","In 2020, which weekday experienced the third-lowest 75th percentile of PM2.5 pollution concentrations?",Monday 7114,8891,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on 25th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2018, what weekday displayed the third-highest 25th percentile for PM2.5 pollution levels?",Tuesday 7115,8892,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on median in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","In 2019, which day of the week corresponded to the third-highest median PM10 pollution levels?",Tuesday 7116,8894,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on 75th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ",Identify the weekday in 2019 that registered the third-highest 75th percentile of PM2.5 pollution levels.,Tuesday 7117,8895,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","In 2018, which weekday was associated with the third-highest 75th percentile of PM10 pollution concentrations?",Thursday 7118,8896,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 75th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","During 2023, determine the weekday that showed the second-highest 75th percentile of PM2.5 pollution levels.",Saturday 7119,8897,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on average in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","For 2019, which weekday experienced the second-lowest average PM2.5 pollution levels?",Monday 7120,8899,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on average in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","In the year 2020, which weekday recorded the second-lowest average PM10 pollution levels?",Sunday 7121,8901,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on 75th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","For 2023, identify the weekday with the second-lowest 75th percentile of PM2.5 pollution levels.",Tuesday 7122,8902,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on median in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","In 2018, which weekday experienced the second-lowest median PM10 pollution concentrations?",Tuesday 7123,8903,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 25th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","During 2020, which weekday saw the lowest 25th percentile of PM2.5 pollution levels?",Sunday 7124,8904,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Considering 2021, what weekday displayed the second-lowest median PM2.5 pollution levels?",Sunday 7125,8905,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2024, which day of the week corresponded to the second-highest median PM10 pollution levels?",Wednesday 7126,8906,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on 75th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","For the year 2019, which weekday had the third-highest 75th percentile of PM10 pollution levels?",Monday 7127,8907,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 75th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ",Identify the weekday in 2018 that registered the second-highest 75th percentile of PM2.5 pollution levels.,Thursday 7128,8909,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 75th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","During 2021, determine the weekday that showed the highest 75th percentile of PM2.5 pollution levels.",Thursday 7129,8910,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 25th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","For 2019, which weekday experienced the highest 25th percentile of PM2.5 pollution levels?",Wednesday 7130,8911,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on average in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Considering 2020, what day of the week had the second-lowest average PM2.5 pollution levels?",Thursday 7131,8912,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on 75th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","In the year 2021, which weekday recorded the third-lowest 75th percentile for PM10 pollution levels?",Monday 7132,8914,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM10 pollution levels on 25th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","For 2018, identify the weekday with the highest 25th percentile of PM10 pollution levels.",Wednesday 7133,8915,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 25th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","In 2023, which weekday experienced the maximum 25th percentile of PM2.5 pollution concentrations?",Wednesday 7134,8916,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on 25th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","During 2019, which weekday saw the lowest 25th percentile of PM2.5 pollution levels?",Sunday 7135,8917,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM2.5 pollution levels on average in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Considering 2022, what weekday displayed the lowest average PM2.5 pollution levels?",Sunday 7136,8918,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on average in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2018, which day of the week corresponded to the second-highest average PM2.5 pollution levels?",Thursday 7137,8920,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 25th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ",Identify the weekday in 2020 that registered the highest 25th percentile of PM2.5 pollution levels.,Wednesday 7138,8922,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on 25th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","During 2023, determine the weekday that showed the second-lowest 25th percentile of PM2.5 pollution levels.",Monday 7139,8923,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on median in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","For 2024, which weekday experienced the second-highest median PM2.5 pollution levels?",Wednesday 7140,8924,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on 75th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2021, what day of the week had the third-highest 75th percentile of PM10 pollution levels?",Tuesday 7141,8926,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM2.5 pollution levels on 25th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ",Which weekday in 2021 was linked to the second-lowest 25th percentile of PM2.5 pollution levels?,Sunday 7142,8927,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on average in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","For 2023, identify the weekday with the second-highest average PM10 pollution levels.",Friday 7143,8928,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on median in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","In 2022, which weekday experienced the third-highest median PM10 pollution concentrations?",Thursday 7144,8929,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on 75th percentile of in 2024?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","During 2024, which weekday saw the third-lowest 75th percentile of PM2.5 pollution levels?",Friday 7145,8930,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the lowest PM10 pollution levels on average in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering 2020, what weekday displayed the lowest average PM10 pollution levels?",Friday 7146,8931,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on average in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","In 2021, which day of the week corresponded to the third-lowest average PM10 pollution levels?",Saturday 7147,8932,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM2.5 pollution levels on 25th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","For the year 2021, which weekday had the third-highest 25th percentile of PM2.5 pollution levels?",Thursday 7148,8933,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on 25th percentile of in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.25).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ",Identify the weekday in 2020 that registered the second-highest 25th percentile of PM2.5 pollution levels.,Tuesday 7149,8934,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd lowest PM10 pollution levels on 75th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","In 2022, which weekday was associated with the second-lowest 75th percentile of PM10 pollution concentrations?",Monday 7150,8936,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on 25th percentile of in 2018?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","For 2018, which weekday experienced the third-lowest 25th percentile of PM10 pollution levels?",Saturday 7151,8937,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd highest PM10 pollution levels on 75th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering 2023, what day of the week had the third-highest 75th percentile of PM10 pollution levels?",Wednesday 7152,8938,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on 75th percentile of in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.75).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","In the year 2019, which weekday recorded the third-lowest 75th percentile for PM10 pollution levels?",Friday 7153,8939,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 75th percentile of in 2022?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ",Which weekday in 2022 was linked to the highest 75th percentile of PM2.5 pollution levels?,Wednesday 7154,8941,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on average in 2020?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2020, which weekday experienced the second-highest average PM10 pollution concentrations?",Tuesday 7155,8942,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM2.5 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].median().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","During 2021, which weekday saw the second-highest median PM2.5 pollution levels?",Wednesday 7156,8943,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM10 pollution levels on 25th percentile of in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].quantile(0.25).reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Considering 2021, what weekday displayed the third-lowest 25th percentile for PM10 pollution levels?",Wednesday 7157,8944,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on average in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].mean().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","In 2021, which day of the week corresponded to the second-highest average PM10 pollution levels?",Friday 7158,8945,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 2nd highest PM10 pollution levels on median in 2021?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.groupby( data['Timestamp'].dt.day_name())[""PM10""].median().reset_index() data = data.dropna(subset=""PM10"") data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","For the year 2021, which weekday had the second-highest median PM10 pollution levels?",Friday 7159,8946,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the 3rd lowest PM2.5 pollution levels on average in 2019?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].mean().reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ",Identify the weekday in 2019 that registered the third-lowest average PM2.5 pollution levels.,Sunday 7160,8948,temporal_aggregation,"Which weekday (i.e. Monday, Tuesday, Wednesday... etc) sees the highest PM2.5 pollution levels on 75th percentile of in 2023?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.groupby( data['Timestamp'].dt.day_name())[""PM2.5""].quantile(0.75).reset_index() data = data.dropna(subset=""PM2.5"") data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","During 2023, determine the weekday that showed the highest 75th percentile of PM2.5 pollution levels.",Sunday 7161,8949,temporal_aggregation,What was the lowest PM2.5 recorded in 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""PM2.5""]) true_code() ","In the year 2021, what was the minimum PM2.5 value observed?",0.06 7162,8950,temporal_aggregation,What was the highest PM2.5 recorded in 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""PM2.5""]) true_code() ","For 2018, what was the peak PM2.5 level recorded?",999.99 7163,8951,temporal_aggregation,What was the 2nd lowest PM2.5 recorded in 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""PM2.5""]) true_code() ",Determine the second-lowest PM2.5 reading from 2020.,0.07 7164,8953,temporal_aggregation,What was the highest PM10 recorded in 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""PM10""]) true_code() ","In 2023, what was the maximum recorded PM10 value?",1000.0 7165,8954,temporal_aggregation,What was the lowest PM2.5 recorded in 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""PM2.5""]) true_code() ","For the year 2018, what was the lowest PM2.5 level noted?",0.02 7166,8955,temporal_aggregation,What was the highest PM10 recorded in 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""PM10""]) true_code() ",What was the highest PM10 reading in 2021?,999.9900000000011 7167,8956,temporal_aggregation,What was the 2nd highest PM2.5 recorded in 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""PM2.5""]) true_code() ",Determine the second-highest PM2.5 value recorded in 2022.,990.0 7168,8957,temporal_aggregation,What was the highest PM2.5 recorded in 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""PM2.5""]) true_code() ","In 2022, what was the peak PM2.5 concentration observed?",1000.0 7169,8958,temporal_aggregation,What was the 2nd lowest PM10 recorded in 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""PM10""]) true_code() ",What was the second-lowest PM10 level measured in 2020?,0.1666666666666666 7170,8960,temporal_aggregation,What was the 3rd lowest PM2.5 recorded in 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""PM2.5""]) true_code() ",Determine the third-lowest PM2.5 reading from 2023.,0.1391044776119401 7171,8961,temporal_aggregation,What was the 3rd highest PM10 recorded in 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""PM10""]) true_code() ",What was the third-highest PM10 concentration measured in 2018?,985.0 7172,8962,temporal_aggregation,What was the lowest PM10 recorded in 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[0][""PM10""]) true_code() ","In 2022, what was the minimum recorded PM10 value?",0.05 7173,8963,temporal_aggregation,What was the 3rd lowest PM10 recorded in 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""PM10""]) true_code() ","For the year 2024, what was the third-lowest PM10 level noted?",0.48 7174,8964,temporal_aggregation,What was the 3rd lowest PM10 recorded in 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[2][""PM10""]) true_code() ",What was the third-lowest PM10 reading in 2023?,0.1999999999999996 7175,8965,temporal_aggregation,What was the 3rd highest PM2.5 recorded in 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""PM2.5""]) true_code() ",Determine the third-highest PM2.5 value recorded in 2023.,985.0 7176,8966,temporal_aggregation,What was the lowest PM2.5 recorded in 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""PM2.5""]) true_code() ","In 2024, what was the peak PM2.5 concentration observed?",0.05 7177,8969,temporal_aggregation,What was the highest PM10 recorded in 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""PM10""]) true_code() ",Determine the highest PM10 reading from 2022.,999.99 7178,8971,temporal_aggregation,What was the 3rd highest PM10 recorded in 2022 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-3][""PM10""]) true_code() ","In 2022, what was the third-highest recorded PM10 value?",995.0 7179,8972,temporal_aggregation,What was the 3rd highest PM2.5 recorded in 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""PM2.5""]) true_code() ","For the year 2018, what was the third-highest PM2.5 level noted?",999.9899999999992 7180,8973,temporal_aggregation,What was the highest PM10 recorded in 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""PM10""]) true_code() ",What was the maximum PM10 reading in 2020?,999.9900000000006 7181,8974,temporal_aggregation,What was the 2nd lowest PM10 recorded in 2023 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[1][""PM10""]) true_code() ",Determine the second-lowest PM10 value recorded in 2023.,0.1999999999999996 7182,8976,temporal_aggregation,What was the 3rd lowest PM2.5 recorded in 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""PM2.5""]) true_code() ",What was the third-lowest PM2.5 level measured in 2021?,0.07 7183,8977,temporal_aggregation,What was the 3rd highest PM2.5 recorded in 2021 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""PM2.5""]) true_code() ","For 2021, what was the third-highest recorded PM2.5 value?",673.4153846153845 7184,8978,temporal_aggregation,What was the 3rd highest PM2.5 recorded in 2024 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2024] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""PM2.5""]) true_code() ",Determine the third-highest PM2.5 reading from 2024.,1000.0 7185,8979,temporal_aggregation,What was the 3rd lowest PM2.5 recorded in 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""PM2.5""]) true_code() ",What was the third-lowest PM2.5 concentration measured in 2020?,0.07 7186,8981,temporal_aggregation,What was the 2nd highest PM2.5 recorded in 2020 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data.dropna(subset=[""PM2.5""]) data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""PM2.5""]) true_code() ","For the year 2020, what was the second-highest PM2.5 level noted?",999.99 7187,8983,temporal_aggregation,What was the highest PM10 recorded in 2018 ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data.dropna(subset=[""PM10""]) data = data.sort_values(by=""PM10"") print(data.iloc[-1][""PM10""]) true_code() ",Determine the highest PM10 value recorded in 2018.,999.99 7188,8984,temporal_aggregation,In which year was the 3rd lowest 75th percentile of PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""]) true_code() ",Which year was associated with the third-lowest 75th percentile for PM2.5 levels?,2020.0 7189,8985,temporal_aggregation,In which year was the lowest average PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify the year when the average PM2.5 level was at its minimum.,2024.0 7190,8987,temporal_aggregation,In which year was the lowest 75th percentile of PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""Timestamp""]) true_code() ",In which year was the 75th percentile of PM2.5 recorded at its lowest point?,2024.0 7191,8988,temporal_aggregation,In which year was the 3rd lowest average PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""]) true_code() ",Which year corresponds to the third-lowest average PM2.5 concentration?,2022.0 7192,8989,temporal_aggregation,In which year was the 2nd lowest median PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""]) true_code() ",Identify the year that saw the second-lowest median PM10 levels.,2020.0 7193,8991,temporal_aggregation,In which year was the 3rd highest 75th percentile of PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""]) true_code() ",In which year was the third-highest 75th percentile for PM2.5 observed?,2019.0 7194,8992,temporal_aggregation,In which year was the highest average PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""Timestamp""]) true_code() ",Which year is linked to the highest recorded average PM10 level?,2018.0 7195,8993,temporal_aggregation,In which year was the 3rd lowest median PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""Timestamp""]) true_code() ",Identify the year associated with the third-lowest median PM2.5.,2020.0 7196,8994,temporal_aggregation,In which year was the 2nd highest average PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""]) true_code() ",During which year was the average PM2.5 level the second highest?,2018.0 7197,8995,temporal_aggregation,In which year was the highest 75th percentile of PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""]) true_code() ",In which year did the 75th percentile of PM2.5 reach its peak value?,2017.0 7198,8997,temporal_aggregation,In which year was the 2nd lowest 75th percentile of PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""Timestamp""]) true_code() ",Identify the year that recorded the second-lowest 75th percentile of PM10.,2023.0 7199,8998,temporal_aggregation,In which year was the 2nd lowest median PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""]) true_code() ",During which year was the median PM2.5 level at its second-lowest?,2023.0 7200,8999,temporal_aggregation,In which year was the 2nd highest median PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""]) true_code() ",In which year did the median PM10 register as the second highest?,2019.0 7201,9000,temporal_aggregation,In which year was the 3rd highest median PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""Timestamp""]) true_code() ",Which year is associated with the third-highest median for PM2.5 levels?,2019.0 7202,9001,temporal_aggregation,In which year was the lowest median PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""Timestamp""]) true_code() ",Identify the year when the median PM10 level was at its minimum.,2024.0 7203,9002,temporal_aggregation,In which year was the 2nd highest 75th percentile of PM10 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM10""]) data = data.groupby(data['Timestamp'].dt.year)['PM10'].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""Timestamp""]) true_code() ",During which year was the 75th percentile of PM10 the second highest?,2019.0 7204,9003,temporal_aggregation,In which year was the 2nd highest median PM2.5 recorded ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.dropna(subset=[""PM2.5""]) data = data.groupby(data['Timestamp'].dt.year)['PM2.5'].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""Timestamp""]) true_code() ",In which year did the median PM2.5 reach its second-highest value?,2018.0 7205,9005,temporal_aggregation,"Across all years, which June had the 2nd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which June experienced the second-highest 25th percentile for PM2.5 levels?",2019.0 7206,9006,temporal_aggregation,"Across all years, which March had the 3rd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which March had the third-lowest 25th percentile of PM10 levels?",2024.0 7207,9007,temporal_aggregation,"Across all years, which May had the lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which May registered the minimum 25th percentile for PM2.5 concentration?",2020.0 7208,9009,temporal_aggregation,"Across all years, which May had the 2nd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which May showed the second-lowest average PM2.5 concentration?",2020.0 7209,9012,temporal_aggregation,"Across all years, which September had the highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which September experienced the highest 25th percentile for PM10 levels?",2018.0 7210,9013,temporal_aggregation,"Across all years, which December had the 3rd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which December was associated with the third-lowest average PM10 concentration?",2022.0 7211,9014,temporal_aggregation,"Across all years, which October had the 2nd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which October showed the second-lowest 25th percentile of PM10 levels?",2024.0 7212,9015,temporal_aggregation,"Across all years, which April had the 2nd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Over all years, which April had the second-lowest 75th percentile for PM10 concentration?",2024.0 7213,9016,temporal_aggregation,"Across all years, which November had the 3rd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which November registered the third-highest median PM10 levels?",2021.0 7214,9017,temporal_aggregation,"Across all years, which June had the 3rd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which June experienced the third-lowest average PM10 concentration?",2023.0 7215,9018,temporal_aggregation,"Across all years, which February had the lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which February was associated with the minimum 25th percentile of PM10 levels?",2024.0 7216,9019,temporal_aggregation,"Across all years, which June had the 2nd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which June showed the second-highest 75th percentile for PM10 concentration?",2019.0 7217,9023,temporal_aggregation,"Across all years, which March had the 2nd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which March was associated with the second-highest median PM10 levels?",2021.0 7218,9024,temporal_aggregation,"Across all years, which October had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which October showed the minimum median PM10 concentration?",2024.0 7219,9025,temporal_aggregation,"Across all years, which November had the highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which November had the highest 25th percentile for PM10 levels?",2018.0 7220,9026,temporal_aggregation,"Across all years, which June had the 2nd lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which June registered the second-lowest median PM2.5 levels?",2020.0 7221,9027,temporal_aggregation,"Across all years, which October had the 3rd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which October experienced the third-highest average PM2.5 concentration?",2020.0 7222,9028,temporal_aggregation,"Across all years, which September had the lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which September was associated with the minimum 25th percentile of PM2.5 levels?",2024.0 7223,9029,temporal_aggregation,"Across all years, which September had the 2nd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which September showed the second-highest average PM10 concentration?",2017.0 7224,9031,temporal_aggregation,"Across all years, which November had the 2nd lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Over all years, which November registered the second-lowest median PM2.5 concentration?",2023.0 7225,9033,temporal_aggregation,"Across all years, which July had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which July was associated with the maximum median PM10 concentration?",2019.0 7226,9034,temporal_aggregation,"Across all years, which September had the lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which September showed the lowest average PM10 levels?",2021.0 7227,9035,temporal_aggregation,"Across all years, which October had the lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which October had the minimum average PM2.5 concentration?",2024.0 7228,9036,temporal_aggregation,"Across all years, which February had the 2nd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which February registered the second-lowest average PM10 levels?",2022.0 7229,9037,temporal_aggregation,"Across all years, which March had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which March experienced the lowest 75th percentile for PM10 concentration?",2020.0 7230,9038,temporal_aggregation,"Across all years, which September had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which September was associated with the third-highest 75th percentile of PM2.5 levels?",2020.0 7231,9039,temporal_aggregation,"Across all years, which September had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which September showed the maximum median PM10 concentration?",2017.0 7232,9040,temporal_aggregation,"Across all years, which March had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which March had the highest 25th percentile for PM2.5 levels?",2018.0 7233,9041,temporal_aggregation,"Across all years, which August had the highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which August registered the maximum 25th percentile for PM10 levels?",2018.0 7234,9042,temporal_aggregation,"Across all years, which February had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Taking all years into account, which February experienced the highest 25th percentile of PM2.5 concentration?",2017.0 7235,9044,temporal_aggregation,"Across all years, which May had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which May showed the lowest 75th percentile for PM10 concentration?",2021.0 7236,9046,temporal_aggregation,"Across all years, which December had the 3rd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which December registered the third-lowest 75th percentile of PM10 levels?",2017.0 7237,9047,temporal_aggregation,"Across all years, which January had the 3rd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Over all years, which January experienced the third-lowest 25th percentile for PM10 concentration?",2023.0 7238,9048,temporal_aggregation,"Across all years, which June had the 2nd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which June was associated with the second-highest median PM10 levels?",2019.0 7239,9049,temporal_aggregation,"Across all years, which April had the 2nd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which April showed the second-highest 25th percentile of PM2.5 levels?",2018.0 7240,9050,temporal_aggregation,"Across all years, which May had the 3rd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which May had the third-highest 25th percentile for PM2.5 concentration?",2017.0 7241,9051,temporal_aggregation,"Across all years, which January had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which January registered the third-highest 75th percentile of PM2.5 levels?",2019.0 7242,9053,temporal_aggregation,"Across all years, which November had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which November was associated with the minimum 75th percentile of PM10 concentration?",2024.0 7243,9054,temporal_aggregation,"Across all years, which January had the 3rd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which January showed the third-lowest 75th percentile of PM2.5 levels?",2020.0 7244,9057,temporal_aggregation,"Across all years, which August had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which August experienced the maximum 25th percentile for PM2.5 concentration?",2018.0 7245,9058,temporal_aggregation,"Across all years, which November had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which November was associated with the third-highest 75th percentile of PM2.5 levels?",2021.0 7246,9060,temporal_aggregation,"Across all years, which March had the 2nd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which March had the second-highest average PM2.5 levels?",2017.0 7247,9061,temporal_aggregation,"Across all years, which May had the 2nd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which May registered the second-lowest 75th percentile for PM2.5 levels?",2020.0 7248,9062,temporal_aggregation,"Across all years, which October had the 2nd highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Taking all years into account, which October experienced the second-highest 25th percentile of PM10 concentration?",2020.0 7249,9063,temporal_aggregation,"Across all years, which September had the highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which September was associated with the maximum 75th percentile of PM10 levels?",2017.0 7250,9064,temporal_aggregation,"Across all years, which October had the highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which October showed the highest 75th percentile for PM2.5 concentration?",2017.0 7251,9065,temporal_aggregation,"Across all years, which August had the highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which August had the highest 75th percentile for PM10 levels?",2018.0 7252,9066,temporal_aggregation,"Across all years, which July had the 3rd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which July registered the third-lowest 75th percentile of PM10 levels?",2020.0 7253,9067,temporal_aggregation,"Across all years, which January had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which January experienced the maximum 25th percentile for PM2.5 concentration?",2018.0 7254,9068,temporal_aggregation,"Across all years, which July had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which July was associated with the highest average PM10 levels?",2019.0 7255,9069,temporal_aggregation,"Across all years, which February had the 2nd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which February showed the second-lowest 75th percentile of PM10 levels?",2022.0 7256,9070,temporal_aggregation,"Across all years, which February had the 2nd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Taking all years into account, which February had the second-highest average PM10 concentration?",2021.0 7257,9072,temporal_aggregation,"Across all years, which October had the 2nd lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which October experienced the second-lowest median PM2.5 concentration?",2022.0 7258,9073,temporal_aggregation,"Across all years, which September had the lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which September was associated with the minimum 25th percentile of PM10 levels?",2021.0 7259,9074,temporal_aggregation,"Across all years, which November had the 2nd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which November showed the second-lowest average PM10 concentration?",2023.0 7260,9075,temporal_aggregation,"Across all years, which December had the 3rd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Over all years, which December had the third-lowest average PM2.5 concentration?",2022.0 7261,9076,temporal_aggregation,"Across all years, which March had the highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which March registered the maximum 75th percentile for PM2.5 levels?",2018.0 7262,9079,temporal_aggregation,"Across all years, which June had the 3rd lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Over all years, which June showed the third-lowest median PM2.5 concentration?",2023.0 7263,9080,temporal_aggregation,"Across all years, which January had the 3rd lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Considering all years, which January had the third-lowest median PM2.5 levels?",2023.0 7264,9081,temporal_aggregation,"Across all years, which May had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which May registered the third-highest 75th percentile for PM2.5 levels?",2019.0 7265,9082,temporal_aggregation,"Across all years, which June had the 3rd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which June experienced the third-highest median PM10 concentration?",2022.0 7266,9083,temporal_aggregation,"Across all years, which October had the highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which October was associated with the maximum 25th percentile of PM10 levels?",2018.0 7267,9084,temporal_aggregation,"Across all years, which October had the 2nd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which October showed the second-highest average PM2.5 concentration?",2018.0 7268,9085,temporal_aggregation,"Across all years, which June had the 2nd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which June had the second-lowest 75th percentile for PM2.5 levels?",2020.0 7269,9086,temporal_aggregation,"Across all years, which November had the 2nd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Taking all years into account, which November registered the second-highest median PM10 levels?",2017.0 7270,9088,temporal_aggregation,"Across all years, which January had the 2nd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which January was associated with the second-lowest 25th percentile of PM10 levels?",2024.0 7271,9089,temporal_aggregation,"Across all years, which March had the 3rd lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which March showed the third-lowest 25th percentile of PM2.5 levels?",2024.0 7272,9090,temporal_aggregation,"Across all years, which April had the lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which April had the minimum average PM10 concentration?",2020.0 7273,9091,temporal_aggregation,"Across all years, which March had the highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which March registered the maximum median PM2.5 levels?",2018.0 7274,9092,temporal_aggregation,"Across all years, which March had the 2nd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which March experienced the second-lowest average PM10 concentration?",2024.0 7275,9093,temporal_aggregation,"Across all years, which September had the 3rd highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which September was associated with the third-highest 25th percentile of PM10 levels?",2020.0 7276,9094,temporal_aggregation,"Across all years, which April had the 3rd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which April showed the third-highest average PM2.5 concentration?",2019.0 7277,9095,temporal_aggregation,"Across all years, which October had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which October had the maximum average PM10 concentration?",2018.0 7278,9096,temporal_aggregation,"Across all years, which January had the lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which January registered the minimum 25th percentile for PM2.5 levels?",2022.0 7279,9097,temporal_aggregation,"Across all years, which April had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which April experienced the lowest 75th percentile for PM10 concentration?",2020.0 7280,9099,temporal_aggregation,"Across all years, which April had the 2nd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which April showed the second-highest median PM2.5 concentration?",2018.0 7281,9100,temporal_aggregation,"Across all years, which December had the 3rd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which December had the third-highest 75th percentile for PM10 levels?",2019.0 7282,9101,temporal_aggregation,"Across all years, which February had the 2nd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which February registered the second-highest median PM2.5 levels?",2017.0 7283,9103,temporal_aggregation,"Across all years, which August had the lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which August was associated with the minimum 25th percentile of PM10 levels?",2020.0 7284,9104,temporal_aggregation,"Across all years, which March had the 2nd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which March showed the second-highest 75th percentile for PM10 concentration?",2021.0 7285,9105,temporal_aggregation,"Across all years, which February had the 3rd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which February had the third-lowest median PM10 levels?",2023.0 7286,9107,temporal_aggregation,"Across all years, which August had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which August experienced the maximum median PM10 concentration?",2018.0 7287,9111,temporal_aggregation,"Across all years, which March had the lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which March registered the minimum 25th percentile for PM10 levels?",2020.0 7288,9112,temporal_aggregation,"Across all years, which September had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which September experienced the lowest 75th percentile for PM10 concentration?",2021.0 7289,9113,temporal_aggregation,"Across all years, which December had the 2nd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which December was associated with the second-lowest median PM10 levels?",2023.0 7290,9114,temporal_aggregation,"Across all years, which February had the lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which February showed the minimum 75th percentile of PM2.5 levels?",2024.0 7291,9115,temporal_aggregation,"Across all years, which July had the highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which July had the maximum 75th percentile for PM10 concentration?",2019.0 7292,9116,temporal_aggregation,"Across all years, which May had the 3rd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Considering all years, which May registered the third-lowest 75th percentile of PM10 levels?",2023.0 7293,9117,temporal_aggregation,"Across all years, which February had the 3rd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which February experienced the third-highest average PM10 concentration?",2017.0 7294,9118,temporal_aggregation,"Across all years, which September had the 3rd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which September was associated with the third-highest average PM10 levels?",2020.0 7295,9119,temporal_aggregation,"Across all years, which June had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which June showed the maximum average PM10 concentration?",2018.0 7296,9120,temporal_aggregation,"Across all years, which February had the 2nd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which February had the second-lowest median PM10 levels?",2022.0 7297,9121,temporal_aggregation,"Across all years, which April had the highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which April registered the maximum 75th percentile for PM10 levels?",2018.0 7298,9123,temporal_aggregation,"Across all years, which April had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which April was associated with the highest average PM10 levels?",2018.0 7299,9124,temporal_aggregation,"Across all years, which April had the highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which April showed the highest 75th percentile for PM2.5 concentration?",2017.0 7300,9125,temporal_aggregation,"Across all years, which September had the 2nd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which September had the second-highest 75th percentile for PM2.5 levels?",2018.0 7301,9126,temporal_aggregation,"Across all years, which December had the 2nd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which December registered the second-lowest 75th percentile of PM10 levels?",2023.0 7302,9127,temporal_aggregation,"Across all years, which April had the 2nd highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which April experienced the second-highest 25th percentile for PM10 concentration?",2019.0 7303,9128,temporal_aggregation,"Across all years, which May had the 3rd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which May was associated with the third-highest median PM2.5 levels?",2019.0 7304,9129,temporal_aggregation,"Across all years, which November had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which November showed the maximum average PM10 concentration?",2018.0 7305,9130,temporal_aggregation,"Across all years, which August had the 2nd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which August had the second-lowest 25th percentile for PM10 levels?",2024.0 7306,9131,temporal_aggregation,"Across all years, which June had the 2nd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which June registered the second-highest median PM2.5 concentration?",2019.0 7307,9132,temporal_aggregation,"Across all years, which May had the 3rd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Considering all years, which May experienced the third-lowest median PM10 levels?",2023.0 7308,9134,temporal_aggregation,"Across all years, which September had the highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Taking all years into account, which September showed the maximum median PM2.5 concentration?",2017.0 7309,9135,temporal_aggregation,"Across all years, which January had the highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which January had the highest average PM2.5 levels?",2018.0 7310,9136,temporal_aggregation,"Across all years, which November had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which November registered the minimum median PM10 concentration?",2024.0 7311,9137,temporal_aggregation,"Across all years, which December had the 2nd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which December experienced the second-highest average PM2.5 levels?",2018.0 7312,9138,temporal_aggregation,"Across all years, which August had the 3rd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which August was associated with the third-lowest 75th percentile of PM2.5 levels?",2022.0 7313,9141,temporal_aggregation,"Across all years, which July had the 2nd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which July registered the second-lowest average PM2.5 concentration?",2023.0 7314,9142,temporal_aggregation,"Across all years, which June had the lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which June experienced the lowest 75th percentile for PM10 levels?",2020.0 7315,9144,temporal_aggregation,"Across all years, which November had the 2nd lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which November showed the second-lowest 25th percentile of PM2.5 concentration?",2024.0 7316,9146,temporal_aggregation,"Across all years, which May had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Taking all years into account, which May registered the maximum 25th percentile of PM2.5 levels?",2018.0 7317,9147,temporal_aggregation,"Across all years, which September had the 2nd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which September experienced the second-highest median PM10 concentration?",2018.0 7318,9148,temporal_aggregation,"Across all years, which July had the lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which July was associated with the minimum 25th percentile of PM2.5 levels?",2024.0 7319,9149,temporal_aggregation,"Across all years, which January had the 2nd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which January showed the second-highest average PM10 concentration?",2018.0 7320,9150,temporal_aggregation,"Across all years, which November had the 3rd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 11] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which November had the third-lowest median PM10 levels?",2022.0 7321,9151,temporal_aggregation,"Across all years, which October had the lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which October registered the minimum 75th percentile of PM2.5 concentration?",2024.0 7322,9152,temporal_aggregation,"Across all years, which March had the 3rd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which March experienced the third-highest average PM10 levels?",2022.0 7323,9153,temporal_aggregation,"Across all years, which April had the 3rd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which April was associated with the third-highest 75th percentile of PM10 levels?",2019.0 7324,9154,temporal_aggregation,"Across all years, which January had the 3rd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which January showed the third-lowest average PM10 concentration?",2020.0 7325,9155,temporal_aggregation,"Across all years, which December had the 2nd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which December had the second-highest average PM10 levels?",2020.0 7326,9157,temporal_aggregation,"Across all years, which February had the highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Across all recorded years, which February experienced the maximum 25th percentile for PM10 levels?",2018.0 7327,9158,temporal_aggregation,"Across all years, which September had the lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which September was associated with the minimum 75th percentile of PM2.5 levels?",2021.0 7328,9159,temporal_aggregation,"Across all years, which December had the highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which December showed the highest 25th percentile for PM2.5 concentration?",2017.0 7329,9160,temporal_aggregation,"Across all years, which December had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which December had the highest median PM10 levels?",2018.0 7330,9161,temporal_aggregation,"Across all years, which October had the 2nd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which October registered the second-lowest 75th percentile for PM2.5 levels?",2021.0 7331,9163,temporal_aggregation,"Across all years, which March had the 3rd highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which March was associated with the third-highest 25th percentile of PM10 levels?",2022.0 7332,9165,temporal_aggregation,"Across all years, which April had the 2nd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which April had the second-lowest 25th percentile for PM10 levels?",2023.0 7333,9166,temporal_aggregation,"Across all years, which June had the 2nd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which June registered the second-lowest average PM2.5 levels?",2023.0 7334,9167,temporal_aggregation,"Across all years, which February had the 3rd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which February experienced the third-highest average PM2.5 concentration?",2021.0 7335,9168,temporal_aggregation,"Across all years, which January had the 2nd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which January was associated with the second-lowest average PM10 levels?",2022.0 7336,9169,temporal_aggregation,"Across all years, which April had the 2nd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 4] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which April showed the second-lowest average PM2.5 concentration?",2024.0 7337,9171,temporal_aggregation,"Across all years, which July had the 3rd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which July registered the third-highest average PM10 concentration?",2017.0 7338,9172,temporal_aggregation,"Across all years, which February had the 2nd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Considering all years, which February experienced the second-lowest 25th percentile for PM10 levels?",2022.0 7339,9176,temporal_aggregation,"Across all years, which February had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which February registered the minimum median PM10 concentration?",2024.0 7340,9178,temporal_aggregation,"Across all years, which August had the 3rd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which August was associated with the third-highest 75th percentile of PM10 levels?",2017.0 7341,9180,temporal_aggregation,"Across all years, which August had the 2nd highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which August had the second-highest average PM2.5 levels?",2017.0 7342,9181,temporal_aggregation,"Across all years, which January had the 2nd lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which January registered the second-lowest median PM10 levels?",2024.0 7343,9182,temporal_aggregation,"Across all years, which February had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which February experienced the third-highest 75th percentile of PM2.5 concentration?",2021.0 7344,9184,temporal_aggregation,"Across all years, which May had the lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which May showed the minimum median PM2.5 concentration?",2020.0 7345,9185,temporal_aggregation,"Across all years, which July had the 3rd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which July had the third-highest 25th percentile for PM2.5 levels?",2017.0 7346,9186,temporal_aggregation,"Across all years, which July had the 2nd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Taking all years into account, which July registered the second-lowest 75th percentile of PM2.5 levels?",2024.0 7347,9187,temporal_aggregation,"Across all years, which June had the lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which June experienced the minimum 25th percentile for PM2.5 concentration?",2020.0 7348,9188,temporal_aggregation,"Across all years, which January had the lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which January was associated with the lowest 25th percentile of PM10 levels?",2022.0 7349,9190,temporal_aggregation,"Across all years, which May had the 2nd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Taking all years into account, which May had the second-highest 25th percentile for PM2.5 concentration?",2019.0 7350,9191,temporal_aggregation,"Across all years, which December had the 2nd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which December registered the second-highest median PM2.5 levels?",2018.0 7351,9195,temporal_aggregation,"Across all years, which May had the highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which May had the highest 75th percentile for PM2.5 concentration?",2017.0 7352,9196,temporal_aggregation,"Across all years, which August had the 2nd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which August registered the second-highest average PM10 levels?",2023.0 7353,9197,temporal_aggregation,"Across all years, which September had the 2nd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Across all recorded years, which September experienced the second-lowest 75th percentile for PM2.5 levels?",2024.0 7354,9198,temporal_aggregation,"Across all years, which March had the highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Taking all years into account, which March was associated with the maximum average PM10 levels?",2018.0 7355,9199,temporal_aggregation,"Across all years, which October had the 3rd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Over all years, which October showed the third-highest median PM10 concentration?",2017.0 7356,9200,temporal_aggregation,"Across all years, which June had the 3rd lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Considering all years, which June had the third-lowest 25th percentile for PM2.5 levels?",2017.0 7357,9201,temporal_aggregation,"Across all years, which May had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which May registered the minimum median PM10 concentration?",2021.0 7358,9202,temporal_aggregation,"Across all years, which August had the 3rd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which August experienced the third-lowest average PM2.5 levels?",2022.0 7359,9203,temporal_aggregation,"Across all years, which October had the lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which October was associated with the minimum median PM2.5 levels?",2024.0 7360,9204,temporal_aggregation,"Across all years, which January had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which January showed the maximum median PM10 concentration?",2019.0 7361,9205,temporal_aggregation,"Across all years, which December had the 3rd lowest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which December had the third-lowest 75th percentile for PM2.5 levels?",2022.0 7362,9206,temporal_aggregation,"Across all years, which July had the highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Taking all years into account, which July registered the maximum median PM2.5 levels?",2018.0 7363,9207,temporal_aggregation,"Across all years, which July had the 2nd lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1]['Timestamp']) true_code() ","Over all years, which July experienced the second-lowest 25th percentile for PM2.5 concentration?",2023.0 7364,9208,temporal_aggregation,"Across all years, which September had the 3rd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Considering all years, which September was associated with the third-lowest average PM2.5 levels?",2023.0 7365,9209,temporal_aggregation,"Across all years, which March had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which March showed the minimum median PM10 concentration?",2020.0 7366,9211,temporal_aggregation,"Across all years, which August had the lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Over all years, which August registered the minimum average PM10 concentration?",2020.0 7367,9212,temporal_aggregation,"Across all years, which October had the 3rd highest 75th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 10] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which October experienced the third-highest 75th percentile for PM2.5 levels?",2020.0 7368,9213,temporal_aggregation,"Across all years, which June had the lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 6] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Across all recorded years, which June was associated with the minimum average PM10 levels?",2020.0 7369,9214,temporal_aggregation,"Across all years, which May had the 3rd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which May showed the third-highest median PM10 concentration?",2022.0 7370,9215,temporal_aggregation,"Across all years, which May had the 3rd lowest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Over all years, which May had the third-lowest average PM10 levels?",2023.0 7371,9216,temporal_aggregation,"Across all years, which March had the 2nd highest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2]['Timestamp']) true_code() ","Considering all years, which March registered the second-highest 25th percentile for PM10 levels?",2019.0 7372,9217,temporal_aggregation,"Across all years, which August had the 2nd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Across all recorded years, which August experienced the second-highest 25th percentile for PM2.5 levels?",2017.0 7373,9218,temporal_aggregation,"Across all years, which December had the 3rd highest average PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 12] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which December was associated with the third-highest average PM10 levels?",2019.0 7374,9219,temporal_aggregation,"Across all years, which September had the 3rd lowest 25th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2]['Timestamp']) true_code() ","Over all years, which September showed the third-lowest 25th percentile of PM10 concentration?",2019.0 7375,9220,temporal_aggregation,"Across all years, which August had the lowest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0]['Timestamp']) true_code() ","Considering all years, which August had the minimum median PM10 levels?",2020.0 7376,9223,temporal_aggregation,"Across all years, which May had the 2nd highest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2]['Timestamp']) true_code() ","Over all years, which May was associated with the second-highest median PM2.5 levels?",2017.0 7377,9224,temporal_aggregation,"Across all years, which February had the 3rd highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 2] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Considering all years, which February showed the third-highest median PM10 concentration?",2021.0 7378,9226,temporal_aggregation,"Across all years, which January had the 3rd lowest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 1] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Taking all years into account, which January registered the third-lowest 25th percentile of PM2.5 levels?",2023.0 7379,9227,temporal_aggregation,"Across all years, which March had the 2nd lowest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1]['Timestamp']) true_code() ","Over all years, which March experienced the second-lowest 75th percentile for PM10 concentration?",2024.0 7380,9228,temporal_aggregation,"Across all years, which March had the highest median PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1]['Timestamp']) true_code() ","Considering all years, which March was associated with the maximum median PM10 levels?",2018.0 7381,9229,temporal_aggregation,"Across all years, which March had the 3rd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 3] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which March showed the third-lowest average PM2.5 concentration?",2023.0 7382,9230,temporal_aggregation,"Across all years, which August had the 3rd highest 25th percentile of PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 8] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3]['Timestamp']) true_code() ","Taking all years into account, which August had the third-highest 25th percentile for PM2.5 concentration?",2023.0 7383,9231,temporal_aggregation,"Across all years, which July had the highest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1]['Timestamp']) true_code() ","Over all years, which July registered the maximum average PM2.5 levels?",2017.0 7384,9233,temporal_aggregation,"Across all years, which September had the 3rd highest 75th percentile of PM10 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 9] data = data.groupby( data['Timestamp'].dt.year)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3]['Timestamp']) true_code() ","Across all recorded years, which September was associated with the third-highest 75th percentile of PM10 levels?",2020.0 7385,9237,temporal_aggregation,"Across all years, which May had the 3rd lowest average PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 5] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2]['Timestamp']) true_code() ","Across all recorded years, which May experienced the third-lowest average PM2.5 levels?",2023.0 7386,9238,temporal_aggregation,"Across all years, which July had the lowest median PM2.5 level?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.month == 7] data = data.groupby( data['Timestamp'].dt.year)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0]['Timestamp']) true_code() ","Taking all years into account, which July was associated with the minimum median PM2.5 levels?",2024.0 7387,9241,temporal_aggregation,"During which month (i.e. January, February, March,...) is the average PM2.5 level the highest across India ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(main_data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""Timestamp""]) true_code() ","For India as a whole, in which month are average PM2.5 levels generally at their peak?",November 7388,9242,temporal_aggregation,"During which month (i.e. January, February, March,...) is the average PM2.5 level the 2nd lowest across India ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(main_data[""Timestamp""].dt.month_name())[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""Timestamp""]) true_code() ","Nationwide, which calendar month is characterized by the second-lowest average PM2.5 levels?",July 7389,9243,temporal_aggregation,"During which month (i.e. January, February, March,...) is the average PM10 level the 3rd highest across India ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data.groupby(main_data[""Timestamp""].dt.month_name())[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""Timestamp""]) true_code() ","Across India, which month typically records the third-highest average PM10 concentration?",January 7390,9246,temporal_aggregation,which week of the year 2022 has the lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Identify the week in 2022 that experienced the minimum 25th percentile for PM2.5 levels.,32.0 7391,9248,temporal_aggregation,which week of the year 2024 has the 2nd lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In 2024, which week of the year was associated with the second-lowest median PM10 levels?",32.0 7392,9249,temporal_aggregation,which week of the year 2021 has the highest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ","During 2021, what week number displayed the maximum 25th percentile for PM10 concentrations?",51.0 7393,9250,temporal_aggregation,which week of the year 2019 has the 2nd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","For 2019, determine the week of the year with the second-lowest median PM2.5 levels.",39.0 7394,9251,temporal_aggregation,which week of the year 2018 has the lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","In the year 2018, which week had the lowest 75th percentile for PM2.5 levels?",34.0 7395,9252,temporal_aggregation,which week of the year 2023 has the lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ",Which week in 2023 recorded the minimum 25th percentile for PM2.5 levels?,30.0 7396,9253,temporal_aggregation,which week of the year 2018 has the 3rd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","Considering 2018, which week of the year had the third-highest average PM10 levels?",45.0 7397,9255,temporal_aggregation,which week of the year 2024 has the highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","For the year 2024, identify the week with the highest median PM2.5 levels.",47.0 7398,9256,temporal_aggregation,which week of the year 2020 has the highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","In 2020, which week of the year experienced the maximum median PM2.5 levels?",52.0 7399,9257,temporal_aggregation,which week of the year 2023 has the 2nd highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","During 2023, which week saw the second-highest 25th percentile for PM2.5 levels?",2.0 7400,9258,temporal_aggregation,which week of the year 2020 has the 2nd highest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","Considering 2020, what week number displayed the second-highest 25th percentile for PM10 concentrations?",53.0 7401,9260,temporal_aggregation,which week of the year 2022 has the 3rd highest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","For the year 2022, which week had the third-highest 25th percentile for PM10 levels?",48.0 7402,9261,temporal_aggregation,which week of the year 2021 has the 3rd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ",Identify the week in 2021 that registered the third-highest median PM10 levels.,45.0 7403,9262,temporal_aggregation,which week of the year 2022 has the 2nd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","In 2022, which week of the year was associated with the second-highest median PM10 concentrations?",49.0 7404,9263,temporal_aggregation,which week of the year 2022 has the 2nd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","During 2022, determine the week number that showed the second-highest median PM2.5 levels.",49.0 7405,9264,temporal_aggregation,which week of the year 2018 has the 2nd lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","For 2018, which week of the year experienced the second-lowest 75th percentile for PM10 levels?",34.0 7406,9265,temporal_aggregation,which week of the year 2023 has the 3rd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","Considering 2023, what week number had the third-highest average PM2.5 levels?",3.0 7407,9266,temporal_aggregation,which week of the year 2022 has the 2nd highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","In the year 2022, which week recorded the second-highest 75th percentile for PM2.5 levels?",52.0 7408,9267,temporal_aggregation,which week of the year 2022 has the lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ",Which week in 2022 was linked to the minimum 25th percentile for PM10 levels?,37.0 7409,9268,temporal_aggregation,which week of the year 2018 has the 2nd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","For 2018, identify the week of the year with the second-highest median PM2.5 levels.",52.0 7410,9269,temporal_aggregation,which week of the year 2019 has the highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","In 2019, which week experienced the maximum median PM2.5 concentrations?",1.0 7411,9270,temporal_aggregation,which week of the year 2024 has the 3rd highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","During 2024, which week saw the third-highest 75th percentile for PM2.5 levels?",4.0 7412,9271,temporal_aggregation,which week of the year 2020 has the 2nd lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","Considering 2020, what week number displayed the second-lowest median PM10 levels?",33.0 7413,9272,temporal_aggregation,which week of the year 2018 has the 3rd lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","In 2018, which week of the year corresponded to the third-lowest 75th percentile for PM10 levels?",30.0 7414,9274,temporal_aggregation,which week of the year 2022 has the highest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ",Identify the week in 2022 that registered the highest 25th percentile for PM10 levels.,11.0 7415,9275,temporal_aggregation,which week of the year 2021 has the 2nd lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In 2021, which week of the year was associated with the second-lowest 25th percentile for PM10 concentrations?",36.0 7416,9278,temporal_aggregation,which week of the year 2019 has the 3rd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","Considering 2019, what week number had the third-highest average PM10 levels?",3.0 7417,9280,temporal_aggregation,which week of the year 2020 has the 3rd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ",Which week in 2020 was linked to the third-highest average PM10 levels?,52.0 7418,9281,temporal_aggregation,which week of the year 2021 has the 2nd lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","For 2021, identify the week of the year with the second-lowest median PM10 levels.",35.0 7419,9282,temporal_aggregation,which week of the year 2019 has the 2nd highest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","In 2019, which week experienced the second-highest 25th percentile for PM10 concentrations?",3.0 7420,9283,temporal_aggregation,which week of the year 2018 has the lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","During 2018, which week saw the lowest 75th percentile for PM10 levels?",29.0 7421,9284,temporal_aggregation,which week of the year 2019 has the 2nd highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","Considering 2019, what week number displayed the second-highest 75th percentile for PM2.5 levels?",1.0 7422,9285,temporal_aggregation,which week of the year 2019 has the highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ","In 2019, which week of the year corresponded to the highest median PM10 levels?",1.0 7423,9286,temporal_aggregation,which week of the year 2021 has the 3rd lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","For the year 2021, which week had the third-lowest 25th percentile for PM2.5 levels?",29.0 7424,9287,temporal_aggregation,which week of the year 2018 has the 2nd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Identify the week in 2018 that registered the second-lowest average PM2.5 levels.,29.0 7425,9288,temporal_aggregation,which week of the year 2019 has the 3rd highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","In 2019, which week of the year was associated with the third-highest 25th percentile for PM2.5 concentrations?",1.0 7426,9289,temporal_aggregation,which week of the year 2023 has the 2nd lowest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","During 2023, determine the week number that showed the second-lowest average PM10 levels.",38.0 7427,9291,temporal_aggregation,which week of the year 2023 has the 2nd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","Considering 2023, what week number had the second-highest median PM2.5 levels?",52.0 7428,9292,temporal_aggregation,which week of the year 2021 has the 3rd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","In the year 2021, which week recorded the third-lowest average PM2.5 levels?",36.0 7429,9294,temporal_aggregation,which week of the year 2019 has the 3rd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","For 2019, identify the week of the year with the third-highest average PM2.5 levels.",46.0 7430,9295,temporal_aggregation,which week of the year 2019 has the lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","In 2019, which week experienced the minimum 25th percentile for PM10 concentrations?",36.0 7431,9296,temporal_aggregation,which week of the year 2024 has the 3rd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","During 2024, which week saw the third-highest median PM10 levels?",45.0 7432,9298,temporal_aggregation,which week of the year 2023 has the 2nd lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In 2023, which week of the year corresponded to the second-lowest 75th percentile for PM10 levels?",38.0 7433,9299,temporal_aggregation,which week of the year 2023 has the 2nd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","For the year 2023, which week had the second-highest average PM2.5 levels?",2.0 7434,9300,temporal_aggregation,which week of the year 2022 has the highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ",Identify the week in 2022 that registered the highest average PM2.5 levels.,48.0 7435,9301,temporal_aggregation,which week of the year 2021 has the 3rd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","In 2021, which week of the year was associated with the third-lowest median PM2.5 concentrations?",37.0 7436,9302,temporal_aggregation,which week of the year 2022 has the lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","During 2022, determine the week number that showed the lowest median PM10 levels.",37.0 7437,9303,temporal_aggregation,which week of the year 2019 has the highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","For 2019, which week of the year experienced the highest 75th percentile for PM2.5 levels?",44.0 7438,9304,temporal_aggregation,which week of the year 2024 has the highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ","Considering 2024, what week number had the highest 75th percentile for PM10 levels?",47.0 7439,9305,temporal_aggregation,which week of the year 2024 has the 2nd highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","In the year 2024, which week recorded the second-highest 25th percentile for PM2.5 levels?",1.0 7440,9307,temporal_aggregation,which week of the year 2022 has the lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","For 2022, identify the week of the year with the lowest 75th percentile for PM2.5 levels.",33.0 7441,9308,temporal_aggregation,which week of the year 2021 has the lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","In 2021, which week experienced the minimum 75th percentile for PM2.5 concentrations?",30.0 7442,9309,temporal_aggregation,which week of the year 2023 has the lowest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","During 2023, which week saw the lowest average PM10 levels?",30.0 7443,9310,temporal_aggregation,which week of the year 2018 has the 2nd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","Considering 2018, what week number displayed the second-highest average PM10 levels?",52.0 7444,9311,temporal_aggregation,which week of the year 2023 has the 2nd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","In 2023, which week of the year corresponded to the second-lowest median PM2.5 levels?",26.0 7445,9312,temporal_aggregation,which week of the year 2019 has the 2nd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","For the year 2019, which week had the second-highest average PM2.5 levels?",1.0 7446,9313,temporal_aggregation,which week of the year 2018 has the 2nd lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Identify the week in 2018 that registered the second-lowest 75th percentile for PM2.5 levels.,29.0 7447,9314,temporal_aggregation,which week of the year 2021 has the lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","In 2021, which week of the year was associated with the minimum 25th percentile for PM10 concentrations?",28.0 7448,9315,temporal_aggregation,which week of the year 2020 has the lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","During 2020, determine the week number that showed the lowest 25th percentile for PM10 levels.",34.0 7449,9316,temporal_aggregation,which week of the year 2022 has the highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","For 2022, which week of the year experienced the highest median PM2.5 levels?",48.0 7450,9317,temporal_aggregation,which week of the year 2024 has the highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","Considering 2024, what week number had the highest average PM2.5 levels?",47.0 7451,9318,temporal_aggregation,which week of the year 2024 has the 3rd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","In the year 2024, which week recorded the third-highest median PM2.5 levels?",46.0 7452,9319,temporal_aggregation,which week of the year 2021 has the 3rd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ",Which week in 2021 was linked to the third-highest average PM2.5 levels?,45.0 7453,9320,temporal_aggregation,which week of the year 2021 has the lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","For 2021, identify the week of the year with the lowest 25th percentile for PM2.5 levels.",30.0 7454,9321,temporal_aggregation,which week of the year 2020 has the 3rd lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","In 2020, which week experienced the third-lowest 75th percentile for PM2.5 concentrations?",33.0 7455,9322,temporal_aggregation,which week of the year 2024 has the 2nd highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","During 2024, which week saw the second-highest 75th percentile for PM10 levels?",46.0 7456,9323,temporal_aggregation,which week of the year 2018 has the 3rd highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-3][""week""]) true_code() ","Considering 2018, what week number displayed the third-highest 25th percentile for PM2.5 levels?",52.0 7457,9324,temporal_aggregation,which week of the year 2019 has the 3rd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","In 2019, which week of the year corresponded to the third-highest median PM10 levels?",44.0 7458,9325,temporal_aggregation,which week of the year 2018 has the 3rd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","For the year 2018, which week had the third-lowest average PM2.5 levels?",33.0 7459,9327,temporal_aggregation,which week of the year 2018 has the 3rd highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","In 2018, which week of the year was associated with the third-highest 75th percentile for PM10 concentrations?",45.0 7460,9328,temporal_aggregation,which week of the year 2023 has the 2nd highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","During 2023, determine the week number that showed the second-highest 75th percentile for PM2.5 levels.",2.0 7461,9329,temporal_aggregation,which week of the year 2019 has the 2nd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","For 2019, which week of the year experienced the second-lowest average PM2.5 levels?",33.0 7462,9330,temporal_aggregation,which week of the year 2019 has the 3rd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","Considering 2019, what week number had the third-lowest median PM2.5 levels?",32.0 7463,9331,temporal_aggregation,which week of the year 2020 has the 2nd lowest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In the year 2020, which week recorded the second-lowest average PM10 levels?",33.0 7464,9332,temporal_aggregation,which week of the year 2018 has the highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ",Which week in 2018 was linked to the highest 75th percentile for PM10 levels?,24.0 7465,9333,temporal_aggregation,which week of the year 2023 has the 2nd lowest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","For 2023, identify the week of the year with the second-lowest 75th percentile for PM2.5 levels.",26.0 7466,9334,temporal_aggregation,which week of the year 2018 has the 2nd lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In 2018, which week experienced the second-lowest median PM10 concentrations?",34.0 7467,9336,temporal_aggregation,which week of the year 2021 has the 2nd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","Considering 2021, what week number displayed the second-lowest median PM2.5 levels?",36.0 7468,9337,temporal_aggregation,which week of the year 2024 has the 2nd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","In 2024, which week of the year corresponded to the second-highest median PM10 levels?",46.0 7469,9338,temporal_aggregation,which week of the year 2019 has the 3rd highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","For the year 2019, which week had the third-highest 75th percentile for PM10 levels?",19.0 7470,9342,temporal_aggregation,which week of the year 2019 has the highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","For 2019, which week of the year experienced the highest 25th percentile for PM2.5 levels?",2.0 7471,9343,temporal_aggregation,which week of the year 2020 has the 2nd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ","Considering 2020, what week number had the second-lowest average PM2.5 levels?",35.0 7472,9345,temporal_aggregation,which week of the year 2020 has the 2nd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ",Which week in 2020 was linked to the second-highest median PM10 levels?,52.0 7473,9348,temporal_aggregation,which week of the year 2019 has the lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","During 2019, which week saw the lowest 25th percentile for PM2.5 levels?",39.0 7474,9349,temporal_aggregation,which week of the year 2022 has the lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[0][""week""]) true_code() ","Considering 2022, what week number displayed the lowest average PM2.5 levels?",32.0 7475,9351,temporal_aggregation,which week of the year 2020 has the 3rd lowest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ","For the year 2020, which week had the third-lowest median PM2.5 levels?",33.0 7476,9352,temporal_aggregation,which week of the year 2020 has the highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ",Identify the week in 2020 that registered the highest 25th percentile for PM2.5 levels.,52.0 7477,9353,temporal_aggregation,which week of the year 2024 has the 3rd lowest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","In 2024, which week of the year was associated with the third-lowest median PM10 concentrations?",30.0 7478,9355,temporal_aggregation,which week of the year 2024 has the 2nd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","For 2024, which week of the year experienced the second-highest median PM2.5 levels?",1.0 7479,9356,temporal_aggregation,which week of the year 2021 has the 3rd highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","Considering 2021, what week number had the third-highest 75th percentile for PM10 levels?",44.0 7480,9357,temporal_aggregation,which week of the year 2020 has the highest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-1][""week""]) true_code() ","In the year 2020, which week recorded the highest 75th percentile for PM10 levels?",45.0 7481,9358,temporal_aggregation,which week of the year 2021 has the 2nd lowest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[1][""week""]) true_code() ",Which week in 2021 was linked to the second-lowest 25th percentile for PM2.5 levels?,28.0 7482,9359,temporal_aggregation,which week of the year 2023 has the 2nd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","For 2023, identify the week of the year with the second-highest average PM10 levels.",2.0 7483,9360,temporal_aggregation,which week of the year 2022 has the 3rd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-3][""week""]) true_code() ","In 2022, which week experienced the third-highest median PM10 concentrations?",11.0 7484,9362,temporal_aggregation,which week of the year 2020 has the lowest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[0][""week""]) true_code() ","Considering 2020, what week number displayed the lowest average PM10 levels?",34.0 7485,9363,temporal_aggregation,which week of the year 2021 has the 3rd lowest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","In 2021, which week of the year corresponded to the third-lowest average PM10 levels?",36.0 7486,9366,temporal_aggregation,which week of the year 2022 has the 2nd lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[1][""week""]) true_code() ","In 2022, which week of the year was associated with the second-lowest 75th percentile for PM10 concentrations?",33.0 7487,9367,temporal_aggregation,which week of the year 2022 has the highest 25th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","During 2022, determine the week number that showed the highest 25th percentile for PM2.5 levels.",48.0 7488,9368,temporal_aggregation,which week of the year 2018 has the 3rd lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","For 2018, which week of the year experienced the third-lowest 25th percentile for PM10 levels?",34.0 7489,9370,temporal_aggregation,which week of the year 2019 has the 3rd lowest 75th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","In the year 2019, which week recorded the third-lowest 75th percentile for PM10 levels?",32.0 7490,9371,temporal_aggregation,which week of the year 2022 has the highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ",Which week in 2022 was linked to the highest 75th percentile for PM2.5 levels?,51.0 7491,9373,temporal_aggregation,which week of the year 2020 has the 2nd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","In 2020, which week experienced the second-highest average PM10 concentrations?",46.0 7492,9374,temporal_aggregation,which week of the year 2021 has the 2nd highest median PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].median().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","During 2021, which week saw the second-highest median PM2.5 levels?",51.0 7493,9375,temporal_aggregation,which week of the year 2021 has the 3rd lowest 25th percentile of PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[2][""week""]) true_code() ","Considering 2021, what week number displayed the third-lowest 25th percentile for PM10 levels?",30.0 7494,9376,temporal_aggregation,which week of the year 2021 has the 2nd highest average PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].mean().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","In 2021, which week of the year corresponded to the second-highest average PM10 levels?",45.0 7495,9377,temporal_aggregation,which week of the year 2021 has the 2nd highest median PM10 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM10""].median().reset_index() data = data.sort_values(by=""PM10"") print(data.iloc[-2][""week""]) true_code() ","For the year 2021, which week had the second-highest median PM10 levels?",53.0 7496,9378,temporal_aggregation,which week of the year 2019 has the 3rd lowest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[2][""week""]) true_code() ",Identify the week in 2019 that registered the third-lowest average PM2.5 levels.,32.0 7497,9379,temporal_aggregation,which week of the year 2021 has the 2nd highest average PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].mean().reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-2][""week""]) true_code() ","In 2021, which week of the year was associated with the second-highest average PM2.5 concentrations?",51.0 7498,9380,temporal_aggregation,which week of the year 2023 has the highest 75th percentile of PM2.5 level ?," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data = data.groupby(data[""Timestamp""].dt.isocalendar().week)[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=""PM2.5"") print(data.iloc[-1][""week""]) true_code() ","During 2023, determine the week number that showed the highest 75th percentile for PM2.5 levels.",1.0 7499,9381,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","In 2022, which of the defined seasons (Winter, Summer, Monsoon, Post-Monsoon) showed the second-highest average PM2.5 levels?",Post-Monsoon 7500,9383,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","Identify the season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) that experienced the minimum 25th percentile of PM2.5 levels.",Monsoon 7501,9384,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","Considering 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) registered the second-highest PM10 levels on average?",Summer 7502,9386,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","During 2021, what season (Winter, Summer, Monsoon, Post-Monsoon) showed the maximum 25th percentile of PM10 concentrations?",Winter 7503,9387,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","For 2019, determine the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-lowest median PM2.5 levels.",Post-Monsoon 7504,9388,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 75th percentile of PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","In the year 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) had the lowest 75th percentile of PM2.5 levels?",Monsoon 7505,9389,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","Which season in 2023 (Winter, Summer, Monsoon, Post-Monsoon) recorded the minimum 25th percentile for PM2.5 levels?",Monsoon 7506,9391,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","In 2018, what season (Winter, Summer, Monsoon, Post-Monsoon) was linked to the third-lowest median PM10 levels?",Summer 7507,9392,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","For the year 2024, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the highest median PM2.5 levels.",Winter 7508,9394,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","During 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the second-highest 25th percentile of PM2.5 levels?",Summer 7509,9395,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","Considering 2020, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the second-highest 25th percentile for PM10 concentrations?",Post-Monsoon 7510,9396,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 25th percentile of PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","In 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the third-lowest 25th percentile of PM2.5 levels?",Summer 7511,9397,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 25th percentile of PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","For the year 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest 25th percentile of PM10 levels?",Post-Monsoon 7512,9398,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","Identify the season in 2021 (Winter, Summer, Monsoon, Post-Monsoon) that registered the third-highest median PM10 levels.",Post-Monsoon 7513,9399,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","In 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-highest median PM10 concentrations?",Winter 7514,9400,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","During 2022, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the second-highest median PM2.5 levels.",Summer 7515,9401,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","For 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-lowest 75th percentile of PM10 levels?",Post-Monsoon 7516,9404,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","Which season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the minimum 25th percentile of PM10 levels?",Monsoon 7517,9405,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","For 2018, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-highest median PM2.5 levels.",Summer 7518,9406,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the maximum median PM2.5 concentrations?",Winter 7519,9407,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","During 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the third-highest 75th percentile of PM2.5 levels?",Summer 7520,9408,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","Considering 2020, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the second-lowest median PM10 levels?",Summer 7521,9409,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","In 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the third-lowest 75th percentile of PM10 levels?",Summer 7522,9411,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","Identify the season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) that registered the highest 25th percentile of PM10 levels.",Summer 7523,9412,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 25th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-lowest 25th percentile of PM10 concentrations?",Post-Monsoon 7524,9413,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest median PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","During 2024, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the lowest median PM10 levels.",Monsoon 7525,9414,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","For 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the highest 75th percentile of PM2.5 levels?",Winter 7526,9415,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","Considering 2019, what season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest average PM10 levels?",Post-Monsoon 7527,9416,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest average PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","In the year 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the third-lowest average PM10 levels?",Summer 7528,9418,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","For 2021, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-lowest median PM10 levels.",Post-Monsoon 7529,9419,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-highest 25th percentile of PM10 concentrations?",Winter 7530,9420,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 75th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","During 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the lowest 75th percentile of PM10 levels?",Monsoon 7531,9421,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 75th percentile of PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","Considering 2019, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the second-highest 75th percentile for PM2.5 levels?",Post-Monsoon 7532,9422,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the highest median PM10 levels?",Winter 7533,9423,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 25th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","For the year 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-lowest 25th percentile of PM2.5 levels?",Summer 7534,9424,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","Identify the season in 2018 (Winter, Summer, Monsoon, Post-Monsoon) that registered the second-lowest average PM2.5 levels.",Summer 7535,9425,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 25th percentile of PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the third-highest 25th percentile of PM2.5 concentrations?",Post-Monsoon 7536,9426,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM10 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","During 2023, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the second-lowest average PM10 levels.",Summer 7537,9427,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 75th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","For 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the lowest 75th percentile of PM2.5 levels?",Monsoon 7538,9428,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","Considering 2023, what season (Winter, Summer, Monsoon, Post-Monsoon) had the second-highest median PM2.5 levels?",Post-Monsoon 7539,9431,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest average PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","For 2019, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the third-highest average PM2.5 levels.",Summer 7540,9432,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the minimum 25th percentile of PM10 concentrations?",Monsoon 7541,9433,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","During 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the third-highest median PM10 levels?",Post-Monsoon 7542,9434,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","Considering 2021, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the third-lowest 75th percentile for PM2.5 levels?",Post-Monsoon 7543,9435,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM10 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the second-lowest 75th percentile of PM10 levels?",Summer 7544,9436,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","For the year 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) had the second-highest average PM2.5 levels?",Post-Monsoon 7545,9437,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest average PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","Identify the season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) that registered the highest average PM2.5 levels.",Winter 7546,9438,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the third-lowest median PM2.5 concentrations?",Post-Monsoon 7547,9439,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest median PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","During 2022, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the lowest median PM10 levels.",Monsoon 7548,9440,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","For 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the highest 75th percentile of PM2.5 levels?",Winter 7549,9441,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","Considering 2024, what season (Winter, Summer, Monsoon, Post-Monsoon) had the highest 75th percentile of PM10 levels?",Winter 7550,9443,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","Which season in 2024 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the third-lowest median PM2.5 levels?",Post-Monsoon 7551,9444,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 75th percentile of PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","For 2022, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the lowest 75th percentile of PM2.5 levels.",Monsoon 7552,9445,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 75th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the minimum 75th percentile of PM2.5 concentrations?",Monsoon 7553,9446,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest average PM10 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","During 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the lowest average PM10 levels?",Monsoon 7554,9447,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","Considering 2018, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the second-highest average PM10 levels?",Summer 7555,9449,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","For the year 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) had the second-highest average PM2.5 levels?",Post-Monsoon 7556,9450,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","Identify the season in 2018 (Winter, Summer, Monsoon, Post-Monsoon) that registered the second-lowest 75th percentile of PM2.5 levels.",Summer 7557,9451,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the minimum 25th percentile of PM10 concentrations?",Monsoon 7558,9452,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","During 2020, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the lowest 25th percentile of PM10 levels.",Monsoon 7559,9453,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest median PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","For 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the highest median PM2.5 levels?",Winter 7560,9454,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest average PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","Considering 2024, what season (Winter, Summer, Monsoon, Post-Monsoon) had the highest average PM2.5 levels?",Winter 7561,9457,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","For 2021, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the lowest 25th percentile of PM2.5 levels.",Monsoon 7562,9458,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","In 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the third-lowest 75th percentile of PM2.5 concentrations?",Post-Monsoon 7563,9459,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 75th percentile of PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","During 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the second-highest 75th percentile of PM10 levels?",Post-Monsoon 7564,9460,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 25th percentile of PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","Considering 2018, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the third-highest 25th percentile for PM2.5 levels?",Post-Monsoon 7565,9462,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest average PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","For the year 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-lowest average PM2.5 levels?",Post-Monsoon 7566,9463,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","Identify the season in 2019 (Winter, Summer, Monsoon, Post-Monsoon) that registered the third-highest 75th percentile of PM2.5 levels.",Summer 7567,9465,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 75th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","During 2023, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the second-highest 75th percentile of PM2.5 levels.",Post-Monsoon 7568,9466,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","For 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-lowest average PM2.5 levels?",Summer 7569,9467,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","Considering 2019, what season (Winter, Summer, Monsoon, Post-Monsoon) had the third-lowest median PM2.5 levels?",Summer 7570,9468,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In the year 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the second-lowest average PM10 levels?",Summer 7571,9469,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","Which season in 2018 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the highest 75th percentile of PM10 levels?",Winter 7572,9470,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","For 2023, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-lowest 75th percentile of PM2.5 levels.",Summer 7573,9471,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-lowest median PM10 concentrations?",Post-Monsoon 7574,9472,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest 25th percentile of PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","During 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the lowest 25th percentile of PM2.5 levels?",Monsoon 7575,9473,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest median PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","Considering 2021, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the second-lowest median PM2.5 levels?",Summer 7576,9474,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM10 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","In 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the second-highest median PM10 levels?",Summer 7577,9475,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","For the year 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest 75th percentile of PM10 levels?",Post-Monsoon 7578,9476,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 75th percentile of PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","Identify the season in 2018 (Winter, Summer, Monsoon, Post-Monsoon) that registered the second-highest 75th percentile of PM2.5 levels.",Post-Monsoon 7579,9477,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM10 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In 2019, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-lowest average PM10 concentrations?",Post-Monsoon 7580,9478,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","During 2021, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the highest 75th percentile of PM2.5 levels.",Winter 7581,9480,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest average PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","Considering 2020, what season (Winter, Summer, Monsoon, Post-Monsoon) had the second-lowest average PM2.5 levels?",Summer 7582,9481,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","In the year 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the third-lowest 75th percentile for PM10 levels?",Post-Monsoon 7583,9482,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","Which season in 2020 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the second-highest median PM10 levels?",Post-Monsoon 7584,9483,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","For 2018, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the highest 25th percentile of PM10 levels.",Winter 7585,9484,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","In 2023, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the maximum 25th percentile of PM2.5 concentrations?",Winter 7586,9486,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest average PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[0][""season""]) true_code() ","Considering 2022, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the lowest average PM2.5 levels?",Monsoon 7587,9487,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM2.5 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","In 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the second-highest average PM2.5 levels?",Post-Monsoon 7588,9488,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest median PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","For the year 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-lowest median PM2.5 levels?",Post-Monsoon 7589,9489,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 25th percentile of PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","Identify the season in 2020 (Winter, Summer, Monsoon, Post-Monsoon) that registered the highest 25th percentile of PM2.5 levels.",Winter 7590,9491,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 25th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","During 2023, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the second-lowest 25th percentile of PM2.5 levels.",Post-Monsoon 7591,9492,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","For 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-highest median PM2.5 levels?",Post-Monsoon 7592,9493,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","Considering 2021, what season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest 75th percentile of PM10 levels?",Summer 7593,9494,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-1][""season""]) true_code() ","In the year 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) recorded the highest 75th percentile for PM10 levels?",Winter 7594,9495,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 25th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","Which season in 2021 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the second-lowest 25th percentile of PM2.5 levels?",Post-Monsoon 7595,9496,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM10 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","For 2023, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-highest average PM10 levels.",Post-Monsoon 7596,9497,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest median PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","In 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the third-highest median PM10 concentrations?",Post-Monsoon 7597,9498,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 75th percentile of PM2.5 levels in 2024 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2024] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","During 2024, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the third-lowest 75th percentile of PM2.5 levels?",Post-Monsoon 7598,9499,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the lowest average PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[0][""season""]) true_code() ","Considering 2020, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the lowest average PM10 levels?",Monsoon 7599,9500,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest average PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the third-lowest average PM10 levels?",Summer 7600,9501,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 25th percentile of PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-3][""season""]) true_code() ","For the year 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest 25th percentile of PM2.5 levels?",Post-Monsoon 7601,9502,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest 25th percentile of PM2.5 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","Identify the season in 2020 (Winter, Summer, Monsoon, Post-Monsoon) that registered the second-highest 25th percentile of PM2.5 levels.",Post-Monsoon 7602,9503,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM10 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[1][""season""]) true_code() ","In 2022, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-lowest 75th percentile of PM10 concentrations?",Post-Monsoon 7603,9505,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 25th percentile of PM10 levels in 2018 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2018] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","For 2018, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the third-lowest 25th percentile of PM10 levels?",Summer 7604,9506,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd highest 75th percentile of PM10 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-3][""season""]) true_code() ","Considering 2023, what season (Winter, Summer, Monsoon, Post-Monsoon) had the third-highest 75th percentile of PM10 levels?",Summer 7605,9508,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM2.5 levels in 2022 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2022] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","Which season in 2022 (Winter, Summer, Monsoon, Post-Monsoon) was linked to the highest 75th percentile of PM2.5 levels?",Winter 7606,9509,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd lowest 75th percentile of PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[1][""season""]) true_code() ","For 2019, identify the season (Winter, Summer, Monsoon, Post-Monsoon) with the second-lowest 75th percentile of PM2.5 levels.",Summer 7607,9510,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM10 levels in 2020 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2020] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","In 2020, which season (Winter, Summer, Monsoon, Post-Monsoon) experienced the second-highest average PM10 concentrations?",Post-Monsoon 7608,9511,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].median().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","During 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) saw the second-highest median PM2.5 levels?",Post-Monsoon 7609,9512,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest 25th percentile of PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].quantile(0.25).reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[2][""season""]) true_code() ","Considering 2021, what season (Winter, Summer, Monsoon, Post-Monsoon) displayed the third-lowest 25th percentile for PM10 levels?",Summer 7610,9513,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].mean().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) corresponded to the second-highest average PM10 levels?",Summer 7611,9514,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest median PM10 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM10""].median().reset_index() data = data.sort_values(by=[""PM10""]) print(data.iloc[-2][""season""]) true_code() ","For the year 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) had the second-highest median PM10 levels?",Summer 7612,9515,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 3rd lowest average PM2.5 levels in 2019 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2019] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[2][""season""]) true_code() ","Identify the season in 2019 (Winter, Summer, Monsoon, Post-Monsoon) that registered the third-lowest average PM2.5 levels.",Post-Monsoon 7613,9516,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the 2nd highest average PM2.5 levels in 2021 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2021] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].mean().reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-2][""season""]) true_code() ","In 2021, which season (Winter, Summer, Monsoon, Post-Monsoon) was associated with the second-highest average PM2.5 concentrations?",Post-Monsoon 7614,9517,temporal_aggregation,"Which season (Winter, Summer, Monsoon, Post-Monsoon) has the highest 75th percentile of PM2.5 levels in 2023 ?"," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data[""Timestamp""].dt.year == 2023] data[""season""] = data[""Timestamp""].dt.month.apply(lambda x: ""Winter"" if x in [10, 11, 12] else ""Summer"" if x in [3, 4, 5] else ""Monsoon"" if x in [6, 7, 8] else ""Post-Monsoon"") data = data.groupby(""season"")[""PM2.5""].quantile(0.75).reset_index() data = data.sort_values(by=[""PM2.5""]) print(data.iloc[-1][""season""]) true_code() ","During 2023, determine the season (Winter, Summer, Monsoon, Post-Monsoon) that showed the highest 75th percentile of PM2.5 levels.",Winter 7615,9519,temporal_aggregation,How many times Madhya Pradesh crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed the Indian guideline for PM10 in 2022?,352 7616,9520,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above 90 µg/m³ of PM10 in the year 2019?,352 7617,9521,temporal_aggregation,How many times Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass the WHO guideline for PM2.5 in 2023?,365 7618,9523,temporal_aggregation,How many times West Bengal crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 30 µg/m³ of PM2.5 in 2018?,305 7619,9524,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass 30 µg/m³ of PM2.5 in the year 2019?,344 7620,9525,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 45 µg/m³ of PM2.5 in 2019?,300 7621,9528,temporal_aggregation,How many times Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 30 µg/m³ of PM2.5 in the year 2018?,365 7622,9531,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed the Indian guideline for PM10 in 2018?,365 7623,9532,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 90 µg/m³ of PM2.5 in the year 2022?,256 7624,9533,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass 75 µg/m³ of PM10 in 2023?,364 7625,9534,temporal_aggregation,How many times Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed the WHO guideline for PM2.5 in the year 2017?,41 7626,9536,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 45 µg/m³ of PM2.5 in the year 2020?,211 7627,9537,temporal_aggregation,How many times Maharashtra crossed the 75 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed 75 µg/m³ of PM10 in 2020?,278 7628,9538,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 30 µg/m³ of PM2.5 in the year 2020?,337 7629,9539,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 90 µg/m³ of PM10 in 2020?,295 7630,9540,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 45 µg/m³ of PM10 in the year 2022?,365 7631,9541,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 45 µg/m³ of PM10 in 2020?,344 7632,9542,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 90 µg/m³ of PM10 in the year 2017?,340 7633,9543,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 75 µg/m³ of PM10 in 2022?,321 7634,9544,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 90 µg/m³ of PM10 in the year 2017?,273 7635,9545,temporal_aggregation,How many times Bihar crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass 90 µg/m³ of PM10 in 2021?,311 7636,9546,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed the Indian guideline for PM10 in the year 2021?,365 7637,9547,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh go above 45 µg/m³ of PM2.5 in 2018?,303 7638,9548,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 90 µg/m³ of PM2.5 in the year 2022?,199 7639,9550,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 90 µg/m³ of PM2.5 in the year 2020?,125 7640,9552,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed the WHO guideline for PM10 in the year 2022?,365 7641,9553,temporal_aggregation,How many times Madhya Pradesh crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh go above the Indian guideline for PM2.5 in 2020?,238 7642,9554,temporal_aggregation,How many times West Bengal crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass the WHO guideline for PM10 in the year 2020?,366 7643,9555,temporal_aggregation,How many times Madhya Pradesh crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed the WHO guideline for PM10 in 2017?,41 7644,9556,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 45 µg/m³ of PM10 in the year 2019?,360 7645,9557,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass 75 µg/m³ of PM2.5 in 2021?,209 7646,9559,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 45 µg/m³ of PM2.5 in 2018?,256 7647,9560,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 90 µg/m³ of PM10 in the year 2023?,365 7648,9561,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed the Indian guideline for PM10 in 2017?,342 7649,9562,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 75 µg/m³ of PM10 in the year 2018?,317 7650,9563,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 45 µg/m³ of PM2.5 in 2023?,358 7651,9567,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 45 µg/m³ of PM2.5 in 2017?,352 7652,9568,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 45 µg/m³ of PM2.5 in the year 2018?,251 7653,9569,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass 45 µg/m³ of PM2.5 in 2022?,358 7654,9570,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed the Indian guideline for PM2.5 in the year 2017?,329 7655,9571,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh go above 45 µg/m³ of PM2.5 in 2017?,41 7656,9572,temporal_aggregation,How many times Madhya Pradesh crossed the WHO guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass the WHO guideline for PM10 in the year 2023?,365 7657,9575,temporal_aggregation,How many times Bihar crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar surpass the WHO guideline for PM10 in 2021?,365 7658,9576,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 45 µg/m³ of PM2.5 in the year 2018?,331 7659,9577,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 75 µg/m³ of PM2.5 in 2018?,242 7660,9578,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 90 µg/m³ of PM10 in the year 2022?,347 7661,9579,temporal_aggregation,How many times Madhya Pradesh crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 30 µg/m³ of PM10 in 2018?,365 7662,9580,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above 90 µg/m³ of PM2.5 in the year 2017?,291 7663,9581,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 45 µg/m³ of PM2.5 in 2023?,313 7664,9582,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 75 µg/m³ of PM10 in the year 2019?,8 7665,9585,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 30 µg/m³ of PM10 in 2017?,0 7666,9586,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 75 µg/m³ of PM10 in the year 2019?,320 7667,9587,temporal_aggregation,How many times Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 30 µg/m³ of PM2.5 in 2023?,365 7668,9588,temporal_aggregation,How many times Madhya Pradesh crossed the Indian guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed the Indian guideline for PM2.5 in the year 2019?,243 7669,9589,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above the Indian guideline for PM2.5 in 2023?,350 7670,9590,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 90 µg/m³ of PM10 in the year 2021?,365 7671,9591,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed the Indian guideline for PM10 in 2021?,354 7672,9592,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 75 µg/m³ of PM2.5 in the year 2017?,255 7673,9593,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 45 µg/m³ of PM10 in 2017?,358 7674,9594,temporal_aggregation,How many times Madhya Pradesh crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 30 µg/m³ of PM10 in the year 2021?,365 7675,9595,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above the Indian guideline for PM10 in 2018?,337 7676,9596,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 45 µg/m³ of PM10 in the year 2023?,365 7677,9597,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 45 µg/m³ of PM10 in 2023?,349 7678,9598,temporal_aggregation,How many times Bihar crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 90 µg/m³ of PM10 in the year 2017?,0 7679,9599,temporal_aggregation,How many times Maharashtra crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 75 µg/m³ of PM10 in 2023?,365 7680,9600,temporal_aggregation,How many times Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 75 µg/m³ of PM2.5 in the year 2017?,37 7681,9602,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass the WHO guideline for PM2.5 in the year 2018?,365 7682,9603,temporal_aggregation,How many times Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed 30 µg/m³ of PM2.5 in 2019?,321 7683,9604,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above the Indian guideline for PM10 in the year 2017?,333 7684,9606,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 45 µg/m³ of PM10 in the year 2021?,365 7685,9607,temporal_aggregation,How many times Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 30 µg/m³ of PM2.5 in 2023?,365 7686,9608,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass the Indian guideline for PM10 in the year 2022?,339 7687,9609,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed the Indian guideline for PM10 in 2019?,344 7688,9610,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above the WHO guideline for PM2.5 in the year 2021?,365 7689,9611,temporal_aggregation,How many times Madhya Pradesh crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 75 µg/m³ of PM2.5 in 2020?,195 7690,9612,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed the Indian guideline for PM10 in the year 2020?,363 7691,9613,temporal_aggregation,How many times Uttar Pradesh crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above the WHO guideline for PM2.5 in 2017?,365 7692,9614,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 45 µg/m³ of PM10 in the year 2017?,362 7693,9615,temporal_aggregation,How many times Maharashtra crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed 30 µg/m³ of PM10 in 2021?,365 7694,9616,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 30 µg/m³ of PM10 in the year 2023?,365 7695,9617,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass the Indian guideline for PM2.5 in 2023?,321 7696,9618,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 90 µg/m³ of PM2.5 in the year 2021?,229 7697,9619,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 45 µg/m³ of PM2.5 in 2017?,260 7698,9620,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass 90 µg/m³ of PM2.5 in the year 2023?,117 7699,9621,temporal_aggregation,How many times West Bengal crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed the WHO guideline for PM2.5 in 2021?,365 7700,9622,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above the Indian guideline for PM2.5 in the year 2022?,335 7701,9624,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 45 µg/m³ of PM2.5 in the year 2017?,0 7702,9625,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above the Indian guideline for PM10 in 2023?,309 7703,9626,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass the Indian guideline for PM2.5 in the year 2018?,342 7704,9627,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 75 µg/m³ of PM2.5 in 2020?,144 7705,9629,temporal_aggregation,How many times West Bengal crossed the Indian guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass the Indian guideline for PM2.5 in 2018?,192 7706,9631,temporal_aggregation,How many times Maharashtra crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 30 µg/m³ of PM2.5 in 2022?,361 7707,9633,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed the WHO guideline for PM2.5 in 2019?,365 7708,9634,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 75 µg/m³ of PM2.5 in the year 2022?,183 7709,9635,temporal_aggregation,How many times Maharashtra crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 45 µg/m³ of PM10 in 2022?,365 7710,9636,temporal_aggregation,How many times Bihar crossed the 75 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 75 µg/m³ of PM2.5 in the year 2023?,330 7711,9637,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 90 µg/m³ of PM2.5 in 2017?,94 7712,9639,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 45 µg/m³ of PM2.5 in 2020?,270 7713,9640,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above the Indian guideline for PM10 in the year 2023?,365 7714,9641,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass 45 µg/m³ of PM10 in 2018?,350 7715,9643,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 90 µg/m³ of PM10 in 2022?,297 7716,9644,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 90 µg/m³ of PM2.5 in the year 2017?,34 7717,9645,temporal_aggregation,How many times Uttar Pradesh crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 75 µg/m³ of PM2.5 in 2019?,292 7718,9646,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above the Indian guideline for PM10 in the year 2023?,365 7719,9647,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass 90 µg/m³ of PM10 in 2018?,302 7720,9648,temporal_aggregation,How many times Madhya Pradesh crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 75 µg/m³ of PM10 in the year 2018?,337 7721,9649,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal go above 90 µg/m³ of PM10 in 2021?,302 7722,9650,temporal_aggregation,How many times West Bengal crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass 90 µg/m³ of PM2.5 in the year 2021?,172 7723,9651,temporal_aggregation,How many times Maharashtra crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed 75 µg/m³ of PM2.5 in 2019?,133 7724,9652,temporal_aggregation,How many times Uttar Pradesh crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above the WHO guideline for PM10 in the year 2019?,365 7725,9653,temporal_aggregation,How many times West Bengal crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass 30 µg/m³ of PM2.5 in 2023?,302 7726,9654,temporal_aggregation,How many times Uttar Pradesh crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 30 µg/m³ of PM2.5 in the year 2022?,365 7727,9655,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above the Indian guideline for PM10 in 2020?,312 7728,9657,temporal_aggregation,How many times Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh exceed 75 µg/m³ of PM10 in 2021?,362 7729,9659,temporal_aggregation,How many times Uttar Pradesh crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass the Indian guideline for PM2.5 in 2020?,301 7730,9660,temporal_aggregation,How many times West Bengal crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 30 µg/m³ of PM2.5 in the year 2020?,273 7731,9662,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass the Indian guideline for PM2.5 in the year 2020?,172 7732,9663,temporal_aggregation,How many times West Bengal crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 75 µg/m³ of PM2.5 in 2018?,162 7733,9664,temporal_aggregation,How many times Bihar crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 90 µg/m³ of PM10 in the year 2019?,8 7734,9665,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 45 µg/m³ of PM10 in 2019?,365 7735,9666,temporal_aggregation,How many times Madhya Pradesh crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 30 µg/m³ of PM2.5 in the year 2018?,339 7736,9668,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh surpass 90 µg/m³ of PM10 in the year 2018?,326 7737,9669,temporal_aggregation,How many times Bihar crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed 30 µg/m³ of PM10 in 2020?,365 7738,9670,temporal_aggregation,How many times Madhya Pradesh crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh go above the WHO guideline for PM2.5 in the year 2021?,365 7739,9672,temporal_aggregation,How many times Bihar crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar exceed the Indian guideline for PM10 in the year 2022?,365 7740,9673,temporal_aggregation,How many times Bihar crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""Bihar""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bihar go above 45 µg/m³ of PM2.5 in 2023?,363 7741,9674,temporal_aggregation,How many times Uttar Pradesh crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh surpass 75 µg/m³ of PM10 in the year 2022?,359 7742,9675,temporal_aggregation,How many times West Bengal crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal exceed 45 µg/m³ of PM2.5 in 2023?,256 7743,9676,temporal_aggregation,How many times Uttar Pradesh crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above 90 µg/m³ of PM2.5 in the year 2022?,262 7744,9677,temporal_aggregation,How many times West Bengal crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""West Bengal""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did West Bengal surpass the WHO guideline for PM10 in 2022?,365 7745,9678,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed the WHO guideline for PM10 in the year 2017?,365 7746,9679,temporal_aggregation,How many times Madhya Pradesh crossed the 45 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh go above 45 µg/m³ of PM2.5 in 2019?,266 7747,9680,temporal_aggregation,How many times Maharashtra crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass 75 µg/m³ of PM10 in the year 2022?,360 7748,9681,temporal_aggregation,How many times Maharashtra crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra exceed 90 µg/m³ of PM10 in 2019?,311 7749,9682,temporal_aggregation,How many times Uttar Pradesh crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['state'] == ""Uttar Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Uttar Pradesh go above 45 µg/m³ of PM10 in the year 2020?,366 7750,9683,temporal_aggregation,How many times Maharashtra crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra surpass the Indian guideline for PM2.5 in 2022?,308 7751,9684,temporal_aggregation,How many times Madhya Pradesh crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Madhya Pradesh""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Madhya Pradesh exceed 90 µg/m³ of PM10 in the year 2017?,41 7752,9685,temporal_aggregation,How many times Maharashtra crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above 75 µg/m³ of PM10 in 2018?,307 7753,9688,temporal_aggregation,How many times Maharashtra crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['state'] == ""Maharashtra""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Maharashtra go above the WHO guideline for PM2.5 in the year 2017?,361 7754,9689,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass 45 µg/m³ of PM10 in the year 2022?,350 7755,9690,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed 45 µg/m³ of PM10 in 2023?,301 7756,9692,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass 90 µg/m³ of PM10 in 2021?,285 7757,9693,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed 45 µg/m³ of PM10 in the year 2017?,262 7758,9694,temporal_aggregation,How many times Ahmedabad city crossed the Indian guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the Indian guideline for PM2.5 in 2019?,158 7759,9695,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass the WHO guideline for PM10 in 2021?,365 7760,9696,temporal_aggregation,How many times Bangalore city crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 90 µg/m³ of PM10 in the year 2019?,0 7761,9698,temporal_aggregation,How many times Hyderabad city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass the Indian guideline for PM10 in 2021?,275 7762,9699,temporal_aggregation,How many times Surat city crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed 30 µg/m³ of PM10 in the year 2020?,0 7763,9700,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the WHO guideline for PM2.5 in 2019?,359 7764,9701,temporal_aggregation,How many times Mumbai city crossed the WHO guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city surpass the WHO guideline for PM2.5 in 2019?,329 7765,9702,temporal_aggregation,How many times Chennai city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed the Indian guideline for PM10 in the year 2017?,0 7766,9703,temporal_aggregation,How many times Pune city crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above the Indian guideline for PM10 in 2022?,230 7767,9704,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass 45 µg/m³ of PM10 in 2018?,0 7768,9707,temporal_aggregation,How many times Kolkata city crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 30 µg/m³ of PM10 in the year 2017?,282 7769,9708,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed the WHO guideline for PM2.5 in 2021?,345 7770,9709,temporal_aggregation,How many times Mumbai city crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above the WHO guideline for PM10 in the year 2022?,365 7771,9710,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass the WHO guideline for PM10 in 2018?,210 7772,9711,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 30 µg/m³ of PM10 in the year 2018?,365 7773,9712,temporal_aggregation,How many times Bangalore city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above 45 µg/m³ of PM10 in 2018?,0 7774,9715,temporal_aggregation,How many times Mumbai city crossed the Indian guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above the Indian guideline for PM2.5 in 2018?,32 7775,9716,temporal_aggregation,How many times Bangalore city crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 90 µg/m³ of PM2.5 in the year 2019?,0 7776,9717,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed the WHO guideline for PM2.5 in 2018?,365 7777,9718,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 90 µg/m³ of PM2.5 in the year 2019?,267 7778,9719,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 90 µg/m³ of PM10 in 2023?,195 7779,9722,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass the Indian guideline for PM10 in 2017?,242 7780,9723,temporal_aggregation,How many times Hyderabad city crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 90 µg/m³ of PM2.5 in the year 2021?,41 7781,9725,temporal_aggregation,How many times Ahmedabad city crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 45 µg/m³ of PM10 in the year 2019?,224 7782,9726,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 30 µg/m³ of PM10 in 2019?,322 7783,9727,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above 90 µg/m³ of PM10 in the year 2018?,328 7784,9729,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed 90 µg/m³ of PM2.5 in the year 2022?,258 7785,9731,temporal_aggregation,How many times Surat city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass the Indian guideline for PM10 in 2017?,0 7786,9732,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 75 µg/m³ of PM2.5 in the year 2020?,31 7787,9733,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 45 µg/m³ of PM2.5 in 2022?,288 7788,9734,temporal_aggregation,How many times Surat city crossed the 90 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass 90 µg/m³ of PM10 in 2020?,0 7789,9735,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 30 µg/m³ of PM2.5 in the year 2017?,301 7790,9736,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the WHO guideline for PM10 in 2019?,365 7791,9738,temporal_aggregation,How many times Chennai city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed the Indian guideline for PM10 in the year 2021?,316 7792,9739,temporal_aggregation,How many times Kolkata city crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above 75 µg/m³ of PM2.5 in 2019?,151 7793,9740,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass the Indian guideline for PM2.5 in 2023?,0 7794,9741,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 45 µg/m³ of PM10 in the year 2022?,252 7795,9743,temporal_aggregation,How many times Bangalore city crossed the 45 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 45 µg/m³ of PM2.5 in 2021?,0 7796,9744,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 90 µg/m³ of PM2.5 in the year 2022?,105 7797,9745,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the WHO guideline for PM10 in 2020?,366 7798,9746,temporal_aggregation,How many times Delhi city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 45 µg/m³ of PM10 in 2018?,365 7799,9747,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed the Indian guideline for PM2.5 in the year 2020?,301 7800,9748,temporal_aggregation,How many times Jaipur city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above the Indian guideline for PM10 in 2021?,342 7801,9749,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass the WHO guideline for PM10 in 2017?,324 7802,9751,temporal_aggregation,How many times Kolkata city crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above 30 µg/m³ of PM10 in 2023?,358 7803,9753,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed 75 µg/m³ of PM10 in the year 2023?,235 7804,9754,temporal_aggregation,How many times Pune city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above the Indian guideline for PM2.5 in 2020?,103 7805,9755,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass the WHO guideline for PM2.5 in 2018?,0 7806,9756,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed the WHO guideline for PM10 in the year 2019?,365 7807,9759,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 45 µg/m³ of PM2.5 in the year 2023?,306 7808,9760,temporal_aggregation,How many times Bangalore city crossed the 75 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above 75 µg/m³ of PM10 in 2020?,0 7809,9761,temporal_aggregation,How many times Kolkata city crossed the 30 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 30 µg/m³ of PM10 in the year 2019?,359 7810,9762,temporal_aggregation,How many times Jaipur city crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 75 µg/m³ of PM2.5 in 2017?,77 7811,9763,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the WHO guideline for PM10 in the year 2023?,365 7812,9764,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 90 µg/m³ of PM2.5 in 2022?,97 7813,9765,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 30 µg/m³ of PM10 in the year 2021?,365 7814,9766,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the WHO guideline for PM2.5 in 2021?,365 7815,9767,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 30 µg/m³ of PM10 in 2018?,188 7816,9768,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 90 µg/m³ of PM10 in the year 2023?,171 7817,9769,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above 45 µg/m³ of PM2.5 in 2017?,224 7818,9770,temporal_aggregation,How many times Delhi city crossed the 45 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 45 µg/m³ of PM2.5 in 2017?,363 7819,9771,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed the WHO guideline for PM10 in the year 2022?,365 7820,9772,temporal_aggregation,How many times Surat city crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 45 µg/m³ of PM10 in 2020?,0 7821,9773,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass the Indian guideline for PM2.5 in 2021?,0 7822,9776,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 45 µg/m³ of PM10 in 2020?,305 7823,9777,temporal_aggregation,How many times Jaipur city crossed the 75 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 75 µg/m³ of PM10 in the year 2017?,126 7824,9779,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass the WHO guideline for PM10 in 2017?,0 7825,9780,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 30 µg/m³ of PM2.5 in the year 2022?,321 7826,9781,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 90 µg/m³ of PM2.5 in 2021?,75 7827,9783,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed the WHO guideline for PM10 in the year 2018?,209 7828,9784,temporal_aggregation,How many times Ahmedabad city crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the Indian guideline for PM10 in 2023?,362 7829,9785,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 30 µg/m³ of PM2.5 in 2023?,327 7830,9786,temporal_aggregation,How many times Delhi city crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed 75 µg/m³ of PM2.5 in the year 2019?,296 7831,9787,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above the WHO guideline for PM10 in 2023?,0 7832,9788,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass the WHO guideline for PM10 in 2020?,366 7833,9789,temporal_aggregation,How many times Bangalore city crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 30 µg/m³ of PM2.5 in the year 2023?,0 7834,9790,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above the WHO guideline for PM2.5 in 2019?,365 7835,9792,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed the Indian guideline for PM10 in 2023?,261 7836,9793,temporal_aggregation,How many times Chennai city crossed the 45 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above 45 µg/m³ of PM2.5 in 2022?,146 7837,9795,temporal_aggregation,How many times Chennai city crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed the Indian guideline for PM2.5 in the year 2017?,171 7838,9796,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 90 µg/m³ of PM2.5 in 2017?,70 7839,9797,temporal_aggregation,How many times Pune city crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass the Indian guideline for PM10 in 2023?,337 7840,9798,temporal_aggregation,How many times Hyderabad city crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed the Indian guideline for PM2.5 in the year 2022?,175 7841,9799,temporal_aggregation,How many times Hyderabad city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above the Indian guideline for PM2.5 in 2020?,100 7842,9801,temporal_aggregation,How many times Surat city crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed 45 µg/m³ of PM10 in the year 2017?,0 7843,9804,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 30 µg/m³ of PM10 in the year 2020?,219 7844,9805,temporal_aggregation,How many times Ahmedabad city crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 45 µg/m³ of PM10 in 2022?,361 7845,9806,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 30 µg/m³ of PM10 in 2023?,365 7846,9807,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 30 µg/m³ of PM10 in the year 2020?,344 7847,9808,temporal_aggregation,How many times Surat city crossed the 30 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 30 µg/m³ of PM10 in 2021?,0 7848,9809,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city surpass 45 µg/m³ of PM10 in 2020?,334 7849,9810,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 90 µg/m³ of PM10 in the year 2020?,245 7850,9811,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 30 µg/m³ of PM10 in 2017?,0 7851,9813,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 75 µg/m³ of PM2.5 in the year 2018?,32 7852,9814,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 90 µg/m³ of PM10 in 2017?,168 7853,9816,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 90 µg/m³ of PM2.5 in the year 2021?,22 7854,9817,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 75 µg/m³ of PM10 in 2018?,0 7855,9818,temporal_aggregation,How many times Surat city crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass 45 µg/m³ of PM2.5 in 2023?,165 7856,9819,temporal_aggregation,How many times Bangalore city crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 75 µg/m³ of PM10 in the year 2019?,0 7857,9823,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above 30 µg/m³ of PM2.5 in 2017?,137 7858,9824,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 90 µg/m³ of PM2.5 in 2019?,127 7859,9825,temporal_aggregation,How many times Surat city crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed 90 µg/m³ of PM10 in the year 2019?,0 7860,9826,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above 45 µg/m³ of PM10 in 2022?,365 7861,9827,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass the WHO guideline for PM10 in 2019?,227 7862,9828,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed 30 µg/m³ of PM10 in the year 2017?,365 7863,9829,temporal_aggregation,How many times Chennai city crossed the WHO guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above the WHO guideline for PM2.5 in 2020?,350 7864,9830,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass 75 µg/m³ of PM10 in 2020?,0 7865,9831,temporal_aggregation,How many times Jaipur city crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 75 µg/m³ of PM2.5 in the year 2021?,127 7866,9832,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 30 µg/m³ of PM10 in 2018?,0 7867,9833,temporal_aggregation,How many times Kolkata city crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 30 µg/m³ of PM2.5 in 2023?,258 7868,9835,temporal_aggregation,How many times Chennai city crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above the WHO guideline for PM10 in 2022?,365 7869,9837,temporal_aggregation,How many times Mumbai city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed the Indian guideline for PM2.5 in the year 2020?,147 7870,9838,temporal_aggregation,How many times Mumbai city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 45 µg/m³ of PM10 in 2018?,207 7871,9839,temporal_aggregation,How many times Surat city crossed the Indian guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city surpass the Indian guideline for PM2.5 in 2019?,0 7872,9840,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 75 µg/m³ of PM2.5 in the year 2023?,59 7873,9841,temporal_aggregation,How many times Surat city crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above the Indian guideline for PM2.5 in 2017?,0 7874,9843,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 90 µg/m³ of PM2.5 in the year 2019?,54 7875,9844,temporal_aggregation,How many times Delhi city crossed the 75 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 75 µg/m³ of PM10 in 2017?,351 7876,9845,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 75 µg/m³ of PM10 in 2019?,210 7877,9846,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 90 µg/m³ of PM10 in the year 2019?,222 7878,9847,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 30 µg/m³ of PM10 in 2020?,357 7879,9848,temporal_aggregation,How many times Mumbai city crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city surpass the Indian guideline for PM10 in 2020?,311 7880,9850,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 90 µg/m³ of PM2.5 in 2018?,115 7881,9853,temporal_aggregation,How many times Surat city crossed the 30 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 30 µg/m³ of PM10 in 2022?,59 7882,9854,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 45 µg/m³ of PM10 in 2018?,151 7883,9856,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above the WHO guideline for PM10 in 2023?,347 7884,9858,temporal_aggregation,How many times Delhi city crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed 45 µg/m³ of PM10 in the year 2019?,365 7885,9859,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above the Indian guideline for PM2.5 in 2020?,0 7886,9860,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 75 µg/m³ of PM10 in 2020?,123 7887,9862,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the Indian guideline for PM10 in 2019?,313 7888,9863,temporal_aggregation,How many times Chennai city crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass the Indian guideline for PM2.5 in 2022?,84 7889,9865,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above 30 µg/m³ of PM2.5 in 2018?,114 7890,9866,temporal_aggregation,How many times Hyderabad city crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 90 µg/m³ of PM2.5 in 2023?,36 7891,9867,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed the WHO guideline for PM2.5 in the year 2022?,365 7892,9868,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above 90 µg/m³ of PM2.5 in 2023?,27 7893,9870,temporal_aggregation,How many times Bangalore city crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 30 µg/m³ of PM2.5 in the year 2020?,0 7894,9871,temporal_aggregation,How many times Surat city crossed the 30 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 30 µg/m³ of PM10 in 2019?,0 7895,9872,temporal_aggregation,How many times Bangalore city crossed the 30 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 30 µg/m³ of PM2.5 in 2022?,0 7896,9873,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed the WHO guideline for PM2.5 in the year 2018?,364 7897,9874,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above 30 µg/m³ of PM10 in 2022?,364 7898,9875,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 90 µg/m³ of PM10 in 2018?,360 7899,9876,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 90 µg/m³ of PM2.5 in the year 2021?,74 7900,9877,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above 90 µg/m³ of PM2.5 in 2017?,71 7901,9878,temporal_aggregation,How many times Jaipur city crossed the 90 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass 90 µg/m³ of PM2.5 in 2021?,83 7902,9879,temporal_aggregation,How many times Ahmedabad city crossed the 45 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city exceed 45 µg/m³ of PM2.5 in the year 2022?,217 7903,9881,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 90 µg/m³ of PM10 in 2021?,316 7904,9882,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 90 µg/m³ of PM2.5 in the year 2022?,24 7905,9883,temporal_aggregation,How many times Jaipur city crossed the 45 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above 45 µg/m³ of PM10 in 2021?,363 7906,9884,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 75 µg/m³ of PM2.5 in 2023?,162 7907,9885,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 45 µg/m³ of PM10 in the year 2018?,358 7908,9888,temporal_aggregation,How many times Mumbai city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed the Indian guideline for PM10 in the year 2017?,244 7909,9889,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above the WHO guideline for PM10 in 2018?,365 7910,9890,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass the Indian guideline for PM2.5 in 2022?,319 7911,9892,temporal_aggregation,How many times Mumbai city crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 75 µg/m³ of PM10 in 2021?,365 7912,9893,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 30 µg/m³ of PM10 in 2023?,365 7913,9894,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed the WHO guideline for PM10 in the year 2021?,0 7914,9896,temporal_aggregation,How many times Hyderabad city crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 45 µg/m³ of PM2.5 in 2020?,179 7915,9897,temporal_aggregation,How many times Kolkata city crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed 30 µg/m³ of PM2.5 in the year 2019?,307 7916,9898,temporal_aggregation,How many times Surat city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above the Indian guideline for PM2.5 in 2020?,0 7917,9900,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed 45 µg/m³ of PM2.5 in the year 2023?,199 7918,9903,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed the Indian guideline for PM10 in the year 2018?,364 7919,9904,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above the WHO guideline for PM10 in 2020?,366 7920,9906,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed the Indian guideline for PM10 in the year 2021?,365 7921,9907,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above 45 µg/m³ of PM2.5 in 2018?,89 7922,9908,temporal_aggregation,How many times Jaipur city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass the Indian guideline for PM10 in 2017?,134 7923,9909,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 30 µg/m³ of PM10 in the year 2019?,357 7924,9910,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the WHO guideline for PM2.5 in 2018?,365 7925,9911,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 75 µg/m³ of PM2.5 in 2017?,105 7926,9912,temporal_aggregation,How many times Jaipur city crossed the 75 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 75 µg/m³ of PM2.5 in the year 2022?,161 7927,9913,temporal_aggregation,How many times Mumbai city crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above the WHO guideline for PM10 in 2017?,333 7928,9914,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 30 µg/m³ of PM10 in 2020?,361 7929,9915,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed the Indian guideline for PM10 in the year 2022?,0 7930,9916,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the WHO guideline for PM10 in 2022?,365 7931,9917,temporal_aggregation,How many times Chennai city crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass 30 µg/m³ of PM2.5 in 2019?,339 7932,9918,temporal_aggregation,How many times Chennai city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed the WHO guideline for PM2.5 in the year 2018?,365 7933,9920,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass 75 µg/m³ of PM2.5 in 2018?,129 7934,9921,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city exceed 75 µg/m³ of PM2.5 in the year 2018?,172 7935,9922,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above 30 µg/m³ of PM2.5 in 2017?,182 7936,9925,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 30 µg/m³ of PM2.5 in 2019?,158 7937,9926,temporal_aggregation,How many times Ahmedabad city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass the Indian guideline for PM10 in 2021?,360 7938,9927,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 90 µg/m³ of PM2.5 in the year 2023?,128 7939,9928,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the WHO guideline for PM2.5 in 2018?,186 7940,9929,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 75 µg/m³ of PM2.5 in 2022?,147 7941,9930,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed 30 µg/m³ of PM10 in the year 2019?,365 7942,9931,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 30 µg/m³ of PM2.5 in 2018?,102 7943,9932,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass the WHO guideline for PM10 in 2021?,365 7944,9933,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed the Indian guideline for PM2.5 in the year 2017?,0 7945,9934,temporal_aggregation,How many times Pune city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above the Indian guideline for PM10 in 2021?,327 7946,9935,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city surpass 90 µg/m³ of PM10 in 2021?,117 7947,9936,temporal_aggregation,How many times Chennai city crossed the 90 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 90 µg/m³ of PM10 in the year 2018?,0 7948,9937,temporal_aggregation,How many times Hyderabad city crossed the 90 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above 90 µg/m³ of PM10 in 2021?,211 7949,9938,temporal_aggregation,How many times Hyderabad city crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass the WHO guideline for PM10 in 2017?,365 7950,9939,temporal_aggregation,How many times Hyderabad city crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city exceed 75 µg/m³ of PM10 in the year 2021?,240 7951,9940,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the WHO guideline for PM2.5 in 2023?,365 7952,9941,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 45 µg/m³ of PM2.5 in 2020?,180 7953,9942,temporal_aggregation,How many times Chennai city crossed the 30 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 30 µg/m³ of PM2.5 in the year 2018?,349 7954,9943,temporal_aggregation,How many times Hyderabad city crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above the Indian guideline for PM10 in 2020?,266 7955,9945,temporal_aggregation,How many times Mumbai city crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 75 µg/m³ of PM10 in the year 2018?,117 7956,9947,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 45 µg/m³ of PM10 in 2019?,339 7957,9948,temporal_aggregation,How many times Bangalore city crossed the WHO guideline of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed the WHO guideline for PM2.5 in the year 2018?,0 7958,9949,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above 45 µg/m³ of PM2.5 in 2020?,145 7959,9950,temporal_aggregation,How many times Mumbai city crossed the 75 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city surpass 75 µg/m³ of PM10 in 2023?,356 7960,9951,temporal_aggregation,How many times Delhi city crossed the 45 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed 45 µg/m³ of PM10 in the year 2020?,366 7961,9952,temporal_aggregation,How many times Delhi city crossed the 75 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 75 µg/m³ of PM2.5 in 2017?,308 7962,9954,temporal_aggregation,How many times Pune city crossed the 75 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 75 µg/m³ of PM10 in the year 2017?,136 7963,9955,temporal_aggregation,How many times Chennai city crossed the Indian guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above the Indian guideline for PM2.5 in 2020?,88 7964,9956,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 90 µg/m³ of PM2.5 in 2017?,0 7965,9958,temporal_aggregation,How many times Kolkata city crossed the Indian guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the Indian guideline for PM2.5 in 2021?,179 7966,9959,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 90 µg/m³ of PM10 in 2023?,261 7967,9960,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city exceed 30 µg/m³ of PM2.5 in the year 2017?,153 7968,9962,temporal_aggregation,How many times Pune city crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city surpass 30 µg/m³ of PM2.5 in 2020?,169 7969,9963,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed the WHO guideline for PM2.5 in the year 2020?,0 7970,9964,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 90 µg/m³ of PM2.5 in 2017?,277 7971,9966,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 90 µg/m³ of PM2.5 in the year 2023?,196 7972,9967,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 30 µg/m³ of PM2.5 in 2019?,343 7973,9968,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 30 µg/m³ of PM2.5 in 2023?,365 7974,9969,temporal_aggregation,How many times Bangalore city crossed the 75 µg/m³ of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 75 µg/m³ of PM10 in the year 2021?,0 7975,9970,temporal_aggregation,How many times Ahmedabad city crossed the Indian guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the Indian guideline for PM10 in 2020?,320 7976,9972,temporal_aggregation,How many times Pune city crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed the WHO guideline for PM2.5 in the year 2017?,255 7977,9973,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the WHO guideline for PM2.5 in 2022?,345 7978,9974,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city surpass 90 µg/m³ of PM2.5 in 2019?,35 7979,9976,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the Indian guideline for PM10 in 2017?,356 7980,9978,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 75 µg/m³ of PM2.5 in the year 2019?,97 7981,9979,temporal_aggregation,How many times Jaipur city crossed the Indian guideline of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above the Indian guideline for PM10 in 2019?,353 7982,9980,temporal_aggregation,How many times Ahmedabad city crossed the 75 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 75 µg/m³ of PM2.5 in 2020?,46 7983,9981,temporal_aggregation,How many times Ahmedabad city crossed the 30 µg/m³ of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city exceed 30 µg/m³ of PM10 in the year 2023?,365 7984,9982,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above the Indian guideline for PM10 in 2018?,0 7985,9983,temporal_aggregation,How many times Bangalore city crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 45 µg/m³ of PM10 in 2019?,0 7986,9984,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city exceed 75 µg/m³ of PM2.5 in the year 2021?,35 7987,9985,temporal_aggregation,How many times Hyderabad city crossed the 75 µg/m³ of PM10 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city go above 75 µg/m³ of PM10 in 2018?,303 7988,9986,temporal_aggregation,How many times Bangalore city crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 75 µg/m³ of PM2.5 in 2021?,0 7989,9987,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed the WHO guideline for PM2.5 in the year 2021?,0 7990,9988,temporal_aggregation,How many times Pune city crossed the 45 µg/m³ of PM10 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city go above 45 µg/m³ of PM10 in 2019?,270 7991,9989,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass 30 µg/m³ of PM2.5 in 2020?,359 7992,9990,temporal_aggregation,How many times Mumbai city crossed the 90 µg/m³ of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city exceed 90 µg/m³ of PM10 in the year 2020?,226 7993,9991,temporal_aggregation,How many times Jaipur city crossed the 45 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above 45 µg/m³ of PM2.5 in 2021?,268 7994,9992,temporal_aggregation,How many times Kolkata city crossed the 90 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass 90 µg/m³ of PM2.5 in 2018?,67 7995,9994,temporal_aggregation,How many times Surat city crossed the 75 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city go above 75 µg/m³ of PM2.5 in 2023?,120 7996,9995,temporal_aggregation,How many times Hyderabad city crossed the 75 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 75 µg/m³ of PM2.5 in 2018?,68 7997,9996,temporal_aggregation,How many times Pune city crossed the Indian guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed the Indian guideline for PM2.5 in the year 2019?,110 7998,9997,temporal_aggregation,How many times Bangalore city crossed the Indian guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city go above the Indian guideline for PM10 in 2021?,0 7999,9998,temporal_aggregation,How many times Hyderabad city crossed the 30 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 30 µg/m³ of PM2.5 in 2023?,365 8000,9999,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city exceed the WHO guideline for PM10 in the year 2017?,0 8001,10000,temporal_aggregation,How many times Delhi city crossed the 30 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 30 µg/m³ of PM2.5 in 2021?,365 8002,10001,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass the WHO guideline for PM2.5 in 2021?,365 8003,10003,temporal_aggregation,How many times Ahmedabad city crossed the 90 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above 90 µg/m³ of PM10 in 2017?,0 8004,10004,temporal_aggregation,How many times Bangalore city crossed the 90 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city surpass 90 µg/m³ of PM2.5 in 2020?,0 8005,10005,temporal_aggregation,How many times Bangalore city crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Bangalore""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Bangalore city exceed 45 µg/m³ of PM10 in the year 2017?,0 8006,10006,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM10 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city go above the WHO guideline for PM10 in 2020?,366 8007,10007,temporal_aggregation,How many times Jaipur city crossed the 30 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city surpass 30 µg/m³ of PM10 in 2017?,141 8008,10009,temporal_aggregation,How many times Delhi city crossed the 90 µg/m³ of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 90 µg/m³ of PM2.5 in 2023?,259 8009,10010,temporal_aggregation,How many times Delhi city crossed the Indian guideline of PM10 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city surpass the Indian guideline for PM10 in 2023?,365 8010,10011,temporal_aggregation,How many times Kolkata city crossed the 45 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city exceed 45 µg/m³ of PM2.5 in the year 2021?,232 8011,10012,temporal_aggregation,How many times Delhi city crossed the 75 µg/m³ of PM2.5 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above 75 µg/m³ of PM2.5 in 2021?,291 8012,10013,temporal_aggregation,How many times Ahmedabad city crossed the 45 µg/m³ of PM10 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 45] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city surpass 45 µg/m³ of PM10 in 2017?,0 8013,10014,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM2.5 in year 2017," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2017] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city exceed the WHO guideline for PM2.5 in the year 2017?,365 8014,10015,temporal_aggregation,How many times Delhi city crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Delhi""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Delhi city go above the WHO guideline for PM10 in 2021?,365 8015,10019,temporal_aggregation,How many times Kolkata city crossed the WHO guideline of PM2.5 in year 2023," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2023] data = data[data['city'] == ""Kolkata""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Kolkata city surpass the WHO guideline for PM2.5 in 2023?,350 8016,10020,temporal_aggregation,How many times Jaipur city crossed the Indian guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 60] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city exceed the Indian guideline for PM10 in the year 2022?,332 8017,10021,temporal_aggregation,How many times Chennai city crossed the 75 µg/m³ of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Chennai""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Chennai city go above 75 µg/m³ of PM10 in 2022?,234 8018,10023,temporal_aggregation,How many times Pune city crossed the 90 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Pune""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Pune city exceed 90 µg/m³ of PM2.5 in the year 2018?,16 8019,10024,temporal_aggregation,How many times Ahmedabad city crossed the WHO guideline of PM10 in year 2021," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2021] data = data[data['city'] == ""Ahmedabad""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Ahmedabad city go above the WHO guideline for PM10 in 2021?,365 8020,10025,temporal_aggregation,How many times Hyderabad city crossed the 75 µg/m³ of PM2.5 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Hyderabad""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 75] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Hyderabad city surpass 75 µg/m³ of PM2.5 in 2022?,97 8021,10026,temporal_aggregation,How many times Surat city crossed the 90 µg/m³ of PM2.5 in year 2018," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2018] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 90] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed 90 µg/m³ of PM2.5 in the year 2018?,0 8022,10027,temporal_aggregation,How many times Jaipur city crossed the WHO guideline of PM10 in year 2022," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2022] data = data[data['city'] == ""Jaipur""] data = data.dropna(subset=""PM10"") data = data[data[""PM10""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Jaipur city go above the WHO guideline for PM10 in 2022?,365 8023,10029,temporal_aggregation,How many times Surat city crossed the WHO guideline of PM2.5 in year 2019," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2019] data = data[data['city'] == ""Surat""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 15] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Surat city exceed the WHO guideline for PM2.5 in the year 2019?,0 8024,10030,temporal_aggregation,How many times Mumbai city crossed the 30 µg/m³ of PM2.5 in year 2020," def true_code(): import numpy as np import pandas as pd main_data = pd.read_pickle(""preprocessed/main_data.pkl"") data = main_data[main_data['Timestamp'].dt.year == 2020] data = data[data['city'] == ""Mumbai""] data = data.dropna(subset=""PM2.5"") data = data[data[""PM2.5""] > 30] count = data['Timestamp'].nunique() print(count) true_code() ",How many times did Mumbai city go above 30 µg/m³ of PM2.5 in 2020?,241