Update app.py

Modified to display each of the last 10 years, using python code and data generated by ChatGPT

app.py

@@ -3,30 +3,81 @@ 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'}
+    {'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': 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}
+    {'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)
 
-# Merge the two DataFrames on the year column
-merged_df = pd.merge(natural_events_df, population_growth_df, on='year')
+# Find the top natural event for each year
+top_event_df = natural_events_df.sort_values(by='year').groupby('year').first()
 
-# Calculate the total population growth for each event and add it to the merged DataFrame
-merged_df['total_growth'] = merged_df['growth_rate'] * 1000000
+# Merge the population growth data with the top event data
+merged_df = pd.merge(top_event_df, population_growth_df, on='year')
 
-# Display the merged DataFrame in the Streamlit app
+# 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())