app.py

import pandas as pd
import streamlit as st

# Define the datasets as Python list dictionaries
natural_events = [
    {'year': 2011, 'location': 'Thailand', 'type': 'Flood', 'effect': 'Economic losses'},
    {'year': 2012, 'location': 'USA', 'type': 'Heat waves', 'effect': 'Crop yield reduction'},
    {'year': 2013, 'location': 'Philippines', 'type': 'Typhoon', 'effect': 'Death toll'},
    {'year': 2014, 'location': 'Brazil', 'type': 'Drought', 'effect': 'Water scarcity'},
    {'year': 2015, 'location': 'Indonesia', 'type': 'Forest fires', 'effect': 'Air pollution'},
    {'year': 2016, 'location': 'Russia', 'type': 'Melting permafrost', 'effect': 'Methane emissions'},
    {'year': 2017, 'location': 'USA', 'type': 'Hurricane', 'effect': 'Property damage'},
    {'year': 2018, 'location': 'Japan', 'type': 'Typhoon', 'effect': 'Economic losses'},
    {'year': 2019, 'location': 'Australia', 'type': 'Wildfires', 'effect': 'Temperature increase'},
    {'year': 2020, 'location': 'Brazil', 'type': 'Deforestation', 'effect': 'CO2 emissions'},
]

population_growth = [
    {'year': 2011, 'country': 'India', 'global_population': 7061000000, 'growth_rate': 1.25},
    {'year': 2012, 'country': 'Ethiopia', 'global_population': 7096000000, 'growth_rate': 2.65},
    {'year': 2013, 'country': 'India', 'global_population': 7125000000, 'growth_rate': 1.26},
    {'year': 2014, 'country': 'Nigeria', 'global_population': 7152000000, 'growth_rate': 2.73},
    {'year': 2015, 'country': 'China', 'global_population': 7182000000, 'growth_rate': 0.52},
    {'year': 2016, 'country': 'India', 'global_population': 7214000000, 'growth_rate': 1.19},
    {'year': 2017, 'country': 'Nigeria', 'global_population': 7253000000, 'growth_rate': 2.61},
    {'year': 2018, 'country': 'India', 'global_population': 7291000000, 'growth_rate': 1.17},
    {'year': 2019, 'country': 'Pakistan', 'global_population': 7329000000, 'growth_rate': 2.04},
    {'year': 2020, 'country': 'Nigeria', 'global_population': 7366000000, 'growth_rate': 2.58},
]

# Convert the datasets to Pandas DataFrames
natural_events_df = pd.DataFrame(natural_events)
population_growth_df = pd.DataFrame(population_growth)

# Find the top natural event for each year
top_event_df = natural_events_df.sort_values(by='year').groupby('year').first()

# Merge the population growth data with the top event data
merged_df = pd.merge(top_event_df, population_growth_df, on='year')

# Define a function to calculate the population increase for a given year and country
def calculate_population_increase(row):
    population_increase = row['global_population'] * (row['growth_rate'] / 100)
    return int(population_increase)

# Apply the population increase calculation to the merged data

merged_df['population_increase'] = merged_df.apply(calculate_population_increase, axis=1)

# Set up the Streamlit app

st.title("Top Natural Events and Population Growth")

# Display the merged data

st.write("Merged Data:")
st.write(merged_df)

 # Perform a join to find the population growth for the top event in each year

top_event_population_growth = pd.merge(top_event_df, population_growth_df, on='year', how='left')
st.write("Population Growth for Top Events:")
st.write(top_event_population_growth)

 # Display a bar chart of the population growth by year

population_chart = pd.DataFrame({'year': population_growth_df['year'], 'population_growth': population_growth_df['growth_rate']})
population_chart.set_index('year', inplace=True)
st.write("Population Growth Chart:")
st.bar_chart(population_chart['population_growth'])

 # Display a pie chart of the top event types

top_event_types = natural_events_df.groupby('type').size().reset_index(name='count')
st.write("Top Event Types:")
st.write(top_event_types)
st.write("Pie Chart of Top Event Types:")
st.plotly_chart(top_event_types, kind='pie', values='count', labels='type')

 # Display a map of the event locations

st.write("Map of Event Locations:")
st.map(natural_events_df[['location']].drop_duplicates())