Create app.py

app.py

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+import pandas as pd
+import streamlit as st
+
+# Define the datasets as Python list dictionaries
+natural_events = [
+    {'location': 'Australia', 'type': 'Wildfires', 'year': 2019, 'effect': 'Temperature increase'},
+    {'location': 'Brazil', 'type': 'Deforestation', 'year': 2020, 'effect': 'CO2 emissions'},
+    {'location': 'Indonesia', 'type': 'Forest fires', 'year': 2015, 'effect': 'Air pollution'},
+    {'location': 'USA', 'type': 'Heat waves', 'year': 2012, 'effect': 'Crop yield reduction'},
+    {'location': 'Russia', 'type': 'Melting permafrost', 'year': 2016, 'effect': 'Methane emissions'}
+]
+
+population_growth = [
+    {'year': 2019, 'country': 'India', 'growth_rate': 1.08},
+    {'year': 2020, 'country': 'Nigeria', 'growth_rate': 2.58},
+    {'year': 2015, 'country': 'China', 'growth_rate': 0.48},
+    {'year': 2012, 'country': 'Ethiopia', 'growth_rate': 2.89},
+    {'year': 2016, 'country': 'India', 'growth_rate': 1.18}
+]
+
+# Convert the datasets to Pandas DataFrames
+natural_events_df = pd.DataFrame(natural_events)
+population_growth_df = pd.DataFrame(population_growth)
+
+# Merge the two DataFrames on the year column
+merged_df = pd.merge(natural_events_df, population_growth_df, on='year')
+
+# Calculate the total population growth for each event and add it to the merged DataFrame
+merged_df['total_growth'] = merged_df['growth_rate'] * 1000000
+
+# Display the merged DataFrame in the Streamlit app
+st.write(merged_df)