Update app.py

app.py

@@ -1,205 +1,205 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-#import bar_chart_race as bcr
-from raceplotly.plots import barplot
-
-
-
-# Configuration and Constants
-COUNTRY_MAPPING = {
-    "Italy, San Marino and the Holy See": "Italy",
-    "France and Monaco": "France",
-    "Belgium and Luxembourg": "Belgium",
-    "China (mainland)": "China",
-    "United States of America": "United States",
-    "United Kingdom of Great Britain and Northern Ireland": "United Kingdom",
-    "Spain and Andorra": "Spain"
-}
-
-DEFAULT_COUNTRIES = ["Italy", "France", "Germany"]
-YEAR_RANGE = (2000, 2020)
-
-# Data Loading and Processing Functions
-@st.cache_data
-def load_data(sheet_name):
-    df = pd.read_excel("dati/fossilco2emission.xlsx", sheet_name=sheet_name)
-    df_mapped = df.copy()
-    df_mapped['Country'] = df_mapped['Country'].replace(COUNTRY_MAPPING)
-    
-    year_cols = list(range(YEAR_RANGE[0], YEAR_RANGE[1] + 1))
-    selected_cols = ['Country'] + year_cols
-    return df_mapped[selected_cols].copy()
-
-def process_data_for_line_plot(df, selected_countries, year_range):
-    mask = df['Country'].isin(selected_countries)
-    filtered_df = df[mask]
-    
-    df_melted = filtered_df.melt(
-        id_vars=['Country'],
-        value_vars=range(year_range[0], year_range[1] + 1),
-        var_name='Year',
-        value_name='Emissions'
-    )
-    df_melted['Year'] = pd.to_numeric(df_melted['Year'])
-    return df_melted
-
-# Visualization Functions
-def create_line_plot(data):
-    fig = px.line(
-        data,
-        x='Year',
-        y='Emissions',
-        color='Country',
-        title='CO2 Emissions Over Time',
-        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'},
-        hover_data={'Year': True, 'Emissions': ':.2f'}
-    )
-    fig.update_layout(height=600, hovermode='x unified')
-    return fig
-
-def create_animated_choropleth(data, start_year, end_year):
-    df_map = data.melt(
-        id_vars=['Country'],
-        value_vars=range(start_year, end_year + 1),
-        var_name='Year',
-        value_name='Emissions'
-    )
-    
-    fig_map = px.choropleth(
-        df_map,
-        locations='Country',
-        locationmode='country names',
-        color='Emissions',
-        animation_frame='Year',
-        title='CO2 Emissions per Capita Over Time',
-        color_continuous_scale='Reds',
-        range_color=[0, df_map['Emissions'].quantile(0.95)],
-        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'}
-    )
-    
-    fig_map.update_layout(
-        height=600,
-        margin=dict(l=0, r=0, t=30, b=0),
-        updatemenus=[{
-            'type': 'buttons',
-            'showactive': False,
-            'buttons': [
-                dict(label='Play',
-                     method='animate',
-                     args=[None, {'frame': {'duration': 500, 'redraw': True},
-                                'fromcurrent': True}]),
-                dict(label='Pause',
-                     method='animate',
-                     args=[[None], {'frame': {'duration': 0, 'redraw': False},
-                                  'mode': 'immediate',
-                                  'transition': {'duration': 0}}])
-            ]
-        }]
-    )
-    return fig_map
-
-
-def create_race_plot(df, year_range):
-    # Prepare data for race plot
-    # Convert year columns to rows for raceplotly format
-    df_race = df.melt(
-        id_vars=['Country'],
-        value_vars=range(year_range[0], year_range[1] + 1),
-        var_name='Year',
-        value_name='Emissions'
-    )
-    
-    # Create the race plot
-    race_plot = barplot(
-        df_race,
-        item_column='Country',
-        value_column='Emissions',
-        time_column='Year',
-        top_entries=10,
-    )
-    
-    # Plot with custom settings
-    fig = race_plot.plot(
-        title='Top 10 Countries by CO2 Emissions',
-        orientation='horizontal',
-        item_label='Country',
-        value_label='CO2 Emissions (Mton)',
-        time_label='Year: ',
-        frame_duration=800
-    )
-
-    # fig.update_layout(
-    # height=700,  # Make plot taller
-    # font=dict(size=12),  # Increase base font size
-    # title_font_size=20,  # Larger title
-    # xaxis_title_font_size=16,  # Larger axis titles
-    # yaxis_title_font_size=16,
-    # yaxis_tickfont_size=14,  # Larger tick labels
-    # xaxis_tickfont_size=14
-    # )
-    
-    return fig
-
-
-# Main App Function
-def main():
-    st.set_page_config(page_title="CO2 Emissions Dashboard", layout="wide")
-    st.title("Global CO2 Emissions Dashboard")
-    
-    # Load Data
-    df1 = load_data("fossil_CO2_per_capita_by_countr")
-    df = load_data("fossil_CO2_totals_by_country")
-    df = df[df['Country'] != 'International Shipping']
-    df2 = df.copy()[:210]
-    
-    # Sidebar Controls
-    #st.sidebar.header("Controls")
-
-    #st.sidebar.markdown("---")
-    st.sidebar.image("dati\SIAM-logo.jpg", width=150)
-
-    visualization_type = st.sidebar.radio(
-        "Choose Visualization",
-        ["Time Series Plot", "Animated World Map", "Bar Chart Race"]
-    )
-    
-    # Year range selector (common to both visualizations)
-    year_range = st.sidebar.slider(
-        "Select Year Range",
-        min_value=YEAR_RANGE[0],
-        max_value=YEAR_RANGE[1],
-        value=YEAR_RANGE
-    )
-    
-    # Conditional controls and display
-    if visualization_type == "Time Series Plot":
-        st.subheader("CO2 Emissions Time Series")
-        
-        # Show country selector only for time series
-        countries = df1['Country'].unique().tolist()
-        selected_countries = st.sidebar.multiselect(
-            "Select countries to compare",
-            options=countries,
-            default=DEFAULT_COUNTRIES
-        )
-        
-        # Process and display time series plot
-        df_processed = process_data_for_line_plot(df1, selected_countries, year_range)
-        fig = create_line_plot(df_processed)
-        st.plotly_chart(fig, use_container_width=True)
-        
-    elif visualization_type == "Animated World Map":
-        st.subheader("Global Emissions Map (Animated)")
-        fig_map = create_animated_choropleth(df1, year_range[0], year_range[1])
-        st.plotly_chart(fig_map, use_container_width=True)
-    else:
-        st.subheader("Top 10 CO2 Emitters Race")
-        fig_race = create_race_plot(df2, year_range)
-        st.plotly_chart(fig_race, use_container_width=True)
-    
-    st.sidebar.markdown("---")
-    st.sidebar.markdown(""" GRUPPO 5 (EMANUELA, FULVIO, MARCO, TINSAE) """)
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+#import bar_chart_race as bcr
+from raceplotly.plots import barplot
+
+
+
+# Configuration and Constants
+COUNTRY_MAPPING = {
+    "Italy, San Marino and the Holy See": "Italy",
+    "France and Monaco": "France",
+    "Belgium and Luxembourg": "Belgium",
+    "China (mainland)": "China",
+    "United States of America": "United States",
+    "United Kingdom of Great Britain and Northern Ireland": "United Kingdom",
+    "Spain and Andorra": "Spain"
+}
+
+DEFAULT_COUNTRIES = ["Italy", "France", "Germany"]
+YEAR_RANGE = (2000, 2020)
+
+# Data Loading and Processing Functions
+@st.cache_data
+def load_data(sheet_name):
+    df = pd.read_excel("dati/fossilco2emission.xlsx", sheet_name=sheet_name)
+    df_mapped = df.copy()
+    df_mapped['Country'] = df_mapped['Country'].replace(COUNTRY_MAPPING)
+    
+    year_cols = list(range(YEAR_RANGE[0], YEAR_RANGE[1] + 1))
+    selected_cols = ['Country'] + year_cols
+    return df_mapped[selected_cols].copy()
+
+def process_data_for_line_plot(df, selected_countries, year_range):
+    mask = df['Country'].isin(selected_countries)
+    filtered_df = df[mask]
+    
+    df_melted = filtered_df.melt(
+        id_vars=['Country'],
+        value_vars=range(year_range[0], year_range[1] + 1),
+        var_name='Year',
+        value_name='Emissions'
+    )
+    df_melted['Year'] = pd.to_numeric(df_melted['Year'])
+    return df_melted
+
+# Visualization Functions
+def create_line_plot(data):
+    fig = px.line(
+        data,
+        x='Year',
+        y='Emissions',
+        color='Country',
+        title='CO2 Emissions Over Time',
+        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'},
+        hover_data={'Year': True, 'Emissions': ':.2f'}
+    )
+    fig.update_layout(height=600, hovermode='x unified')
+    return fig
+
+def create_animated_choropleth(data, start_year, end_year):
+    df_map = data.melt(
+        id_vars=['Country'],
+        value_vars=range(start_year, end_year + 1),
+        var_name='Year',
+        value_name='Emissions'
+    )
+    
+    fig_map = px.choropleth(
+        df_map,
+        locations='Country',
+        locationmode='country names',
+        color='Emissions',
+        animation_frame='Year',
+        title='CO2 Emissions per Capita Over Time',
+        color_continuous_scale='Reds',
+        range_color=[0, df_map['Emissions'].quantile(0.95)],
+        labels={'Emissions': 'CO2 Emissions per Capita (Mton)'}
+    )
+    
+    fig_map.update_layout(
+        height=600,
+        margin=dict(l=0, r=0, t=30, b=0),
+        updatemenus=[{
+            'type': 'buttons',
+            'showactive': False,
+            'buttons': [
+                dict(label='Play',
+                     method='animate',
+                     args=[None, {'frame': {'duration': 500, 'redraw': True},
+                                'fromcurrent': True}]),
+                dict(label='Pause',
+                     method='animate',
+                     args=[[None], {'frame': {'duration': 0, 'redraw': False},
+                                  'mode': 'immediate',
+                                  'transition': {'duration': 0}}])
+            ]
+        }]
+    )
+    return fig_map
+
+
+def create_race_plot(df, year_range):
+    # Prepare data for race plot
+    # Convert year columns to rows for raceplotly format
+    df_race = df.melt(
+        id_vars=['Country'],
+        value_vars=range(year_range[0], year_range[1] + 1),
+        var_name='Year',
+        value_name='Emissions'
+    )
+    
+    # Create the race plot
+    race_plot = barplot(
+        df_race,
+        item_column='Country',
+        value_column='Emissions',
+        time_column='Year',
+        top_entries=10,
+    )
+    
+    # Plot with custom settings
+    fig = race_plot.plot(
+        title='Top 10 Countries by CO2 Emissions',
+        orientation='horizontal',
+        item_label='Country',
+        value_label='CO2 Emissions (Mton)',
+        time_label='Year: ',
+        frame_duration=800
+    )
+
+    # fig.update_layout(
+    # height=700,  # Make plot taller
+    # font=dict(size=12),  # Increase base font size
+    # title_font_size=20,  # Larger title
+    # xaxis_title_font_size=16,  # Larger axis titles
+    # yaxis_title_font_size=16,
+    # yaxis_tickfont_size=14,  # Larger tick labels
+    # xaxis_tickfont_size=14
+    # )
+    
+    return fig
+
+
+# Main App Function
+def main():
+    st.set_page_config(page_title="CO2 Emissions Dashboard", layout="wide")
+    st.title("Global CO2 Emissions Dashboard")
+    
+    # Load Data
+    df1 = load_data("fossil_CO2_per_capita_by_countr")
+    df = load_data("fossil_CO2_totals_by_country")
+    df = df[df['Country'] != 'International Shipping']
+    df2 = df.copy()[:210]
+    
+    # Sidebar Controls
+    #st.sidebar.header("Controls")
+
+    #st.sidebar.markdown("---")
+    st.sidebar.image("dati/SIAM-logo.jpg", width=150)
+
+    visualization_type = st.sidebar.radio(
+        "Choose Visualization",
+        ["Time Series Plot", "Animated World Map", "Bar Chart Race"]
+    )
+    
+    # Year range selector (common to both visualizations)
+    year_range = st.sidebar.slider(
+        "Select Year Range",
+        min_value=YEAR_RANGE[0],
+        max_value=YEAR_RANGE[1],
+        value=YEAR_RANGE
+    )
+    
+    # Conditional controls and display
+    if visualization_type == "Time Series Plot":
+        st.subheader("CO2 Emissions Time Series")
+        
+        # Show country selector only for time series
+        countries = df1['Country'].unique().tolist()
+        selected_countries = st.sidebar.multiselect(
+            "Select countries to compare",
+            options=countries,
+            default=DEFAULT_COUNTRIES
+        )
+        
+        # Process and display time series plot
+        df_processed = process_data_for_line_plot(df1, selected_countries, year_range)
+        fig = create_line_plot(df_processed)
+        st.plotly_chart(fig, use_container_width=True)
+        
+    elif visualization_type == "Animated World Map":
+        st.subheader("Global Emissions Map (Animated)")
+        fig_map = create_animated_choropleth(df1, year_range[0], year_range[1])
+        st.plotly_chart(fig_map, use_container_width=True)
+    else:
+        st.subheader("Top 10 CO2 Emitters Race")
+        fig_race = create_race_plot(df2, year_range)
+        st.plotly_chart(fig_race, use_container_width=True)
+    
+    st.sidebar.markdown("---")
+    st.sidebar.markdown(""" GRUPPO 5 (EMANUELA, FULVIO, MARCO, TINSAE) """)
+
+if __name__ == "__main__":
+    main()