PyCafe - Dash - Dash-Based Carbon Emissions Analysis

Carbon_Emissions_Global_from_1850.csv
Carbon_Emissions_National_Totals_2021.csv
Carbon_Emissions_per_capita_2021.csv
app.py
requirements.txt
app.py
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import dash
from dash import dcc, html, Input, Output
import pandas as pd
import plotly.express as px

# Load per capita data
df_per_capita = pd.read_csv('Carbon_Emissions_per_capita_2021.csv')
df_per_capita.rename(columns={
    'Per_capita_CO2_emissions_metric_tons': 'PerCapitaEmissions'
}, inplace=True)
df_per_capita = df_per_capita.dropna(subset=['PerCapitaEmissions'])
df_per_capita = df_per_capita.sort_values('PerCapitaEmissions', ascending=False).head(10)

# Load total emissions data
df_total = pd.read_csv('Carbon_Emissions_National_Totals_2021.csv')
df_total.rename(columns={'Total': 'TotalEmissions'}, inplace=True)
df_total = df_total.dropna(subset=['TotalEmissions'])
df_total = df_total.sort_values('TotalEmissions', ascending=False).head(10)

# Reusable style header
style_header = {'textAlign': 'center', 'fontFamily': 'Arial, sans-serif'}


# Initialize app
app = dash.Dash(__name__)
app.title = "Global Carbon Emissions"

app.layout = html.Div([
    html.H1("Global Carbon Emissions", style=style_header),

    html.H2("Global Fossil Fuel Emissions Over Time", style=style_header),
    html.Div(
        dcc.Graph(
            id='emissions-line-chart',
            figure=px.line(
                pd.read_csv('Carbon_Emissions_Global_from_1850.csv').query('Year >= 1850').melt(
                    id_vars='Year',
                    value_vars=['Solid', 'Liquid', 'Gas', 'Cement', 'Gas_Flaring'],
                    var_name='Category', value_name='Emissions'
                ),
                x='Year', y='Emissions', color='Category',
                title='Carbon Emissions by Category Over Time'
            )
        ),
        style={'width': '75%', 'margin': 'auto'}
    ),

    html.H2("Top 20 CO₂ Emitters by Country (2021)", style=style_header),
    html.Div([
        dcc.RadioItems(
            id='emissions-view-selector',
            options=[
                {'label': 'Per Capita Emissions', 'value': 'per_capita'},
                {'label': 'Total Emissions', 'value': 'total'}
            ],
            value='per_capita',
            labelStyle={'display': 'inline-block', 'margin-right': '20px'}
        )
    ], style={'width': '75%', 'margin': 'auto', 'textAlign': 'center', 'padding': '10px 0'}),

    html.Div(
        dcc.Graph(id='bubble-chart'),
        style={'width': '75%', 'margin': 'auto',
               'fontFamily': 'Arial, sans-serif'}
    )
], style={'backgroundColor': '#a4c8e4'})

@app.callback(
    Output('bubble-chart', 'figure'),
    Input('emissions-view-selector', 'value')
)
def update_bubble_chart(view):
    if view == 'per_capita':
        fig = px.scatter(
            df_per_capita,
            x='Nation',
            y='PerCapitaEmissions',
            size='PerCapitaEmissions',
            hover_name='Nation',
            text='Nation',
            title='Note: Total Amount (includes all categories)',
            labels={'PerCapitaEmissions': 'Metric Tons CO₂ per Person'}
        )
    else:
        fig = px.scatter(
            df_total,
            x='Nation',
            y='TotalEmissions',
            size='TotalEmissions',
            hover_name='Nation',
            text='Nation',
            title='Note: Total Amount (includes all categories)',
            labels={'TotalEmissions': 'Total Metric Tons CO₂'}
        )

    
    fig.update_layout(height=800)
    fig.update_traces(
        textposition='top center',
        textfont=dict(color='black', size=11)
)
    fig.update_traces(
    textposition='top center',
    textfont=dict(color='black', size=12)
    )
    fig.update_layout(
        xaxis={'visible': False},
        margin=dict(l=40, r=40, t=60, b=40)
    )
    return fig

if __name__ == '__main__':
    app.run(debug=True)
Py.Cafe

dash-carbon-emissions-analysis

Dash-Based Carbon Emissions Analysis
