Py.Cafe

import pandas as pd
import plotly.express as px
from dash import Dash, dcc, html, Input, Output

df = pd.read_csv('https://py.cafe/files/Feanor1992/Green Climate Fund/ODL-Export-Countries.csv')

# Convert columns to numeric
df['RP Financing $'] = pd.to_numeric(df['RP Financing $'], errors='coerce')
df['FA Financing $'] = pd.to_numeric(df['FA Financing $'], errors='coerce')

# Handle missing values in the financing columns
df['RP Financing $'] = df['RP Financing $'].fillna(0)
df['FA Financing $'] = df['FA Financing $'].fillna(0)

# Create a new column for total financing
df['Total Financing $'] = df['RP Financing $'] + df['FA Financing $']
df['Total Financing $'] = pd.to_numeric(df['Total Financing $'])

# Analyze data by region
region_summary = df.groupby('Region').agg(
    total_financing=('Total Financing $', 'sum'),
    avg_financing=('Total Financing $', 'mean'),
    num_countries=('Country Name', 'count')
).reset_index()

region_summary['total_financing'] = pd.to_numeric(region_summary['total_financing'])
region_summary['avg_financing'] = pd.to_numeric(region_summary['avg_financing'])

# Analyze SIDS and LDCs
sids_ldcs_summary = df.groupby(['SIDS', 'LDCs']).agg(
    total_financing=('Total Financing $', 'sum'),
    avg_financing=('Total Financing $', 'mean'),
    num_countries=('Country Name', 'count')
).reset_index()

sids_ldcs_summary['total_financing'] = pd.to_numeric(sids_ldcs_summary['total_financing'])
sids_ldcs_summary['avg_financing'] = pd.to_numeric(sids_ldcs_summary['avg_financing'])

# Identify top countries by total financing
top_countries = df.nlargest(
    10,
    'Total Financing $'
)[['Country Name', 'Total Financing $']]

top_countries['Total Financing $'] = pd.to_numeric(top_countries['Total Financing $'])

# Analyze financing distribution
financing_distribution = df[['RP Financing $', 'FA Financing $']].describe().T
financing_distribution = financing_distribution.round(2)

# Create a Dash app
app = Dash(__name__)
app.title = "Green Climate Analysis"

# Choropleth Map
choropleth_map = px.choropleth(
    data_frame=df,
    locations='ISO3',
    color='Total Financing $',
    color_continuous_scale='Mint',
    hover_name='Country Name',
    hover_data={
        'Region': True,
        'SIDS': True,
        'LDCs': True,
        'RP Financing $': ':.2f',
        'FA Financing $': True,
        'Total Financing $': ':.2f'
    },
    title='Total Financing by Country',
    projection='orthographic'
)
choropleth_map.update_layout(
    template="plotly_dark",
    font=dict(color="white"),
    title_font_size=20,
    paper_bgcolor="rgba(0, 0, 0, 0)",
    geo_bgcolor="rgba(0, 0, 0, 0)"
)

# Financing Distribution
financing_distribution_chart = px.bar(
    df,
    x='Country Name',
    y=['RP Financing $', 'FA Financing $'],
    barmode='group',
    title='Financing Distribution'
)
financing_distribution_chart. update_layout(
    template='plotly_dark',
    font=dict(color='white'),
    title_font_size=20,
    paper_bgcolor='rgba(0, 0, 0, 0)'
)

# Total Financing by Region
region_bar_chart = px.bar(
    region_summary,
    x='Region',
    y='total_financing',
    text='num_countries',
    title='Total Financing by Region',
    labels={
        'total_financing': 'Total Financing $',
        'num_countries': 'Number of Countries'
    },
    color='avg_financing',
    color_continuous_scale='plasma'
)
region_bar_chart.update_traces(
    texttemplate='%{text} countries',
    textposition='outside'
)
region_bar_chart.update_layout(
    template="plotly_dark",
    xaxis_title='Region',
    yaxis_title='Total Financing $'
)

# SIDS and LDCs Bar Chart
sids_ldcs_bar_chart = px.bar(
    sids_ldcs_summary,
    x=['SIDS', 'LDCs'],
    y='total_financing',
    color='num_countries',
    title='SIDS and LDCs Financing'
)
sids_ldcs_bar_chart.update_layout(
    template='plotly_dark',
    font=dict(color='white'),
    title_font_size=20,
    paper_bgcolor='rgba(0, 0, 0, 0)'
)

# RP vs FA Financing
rp_vs_fa_scatter = px.scatter(
    df,
    x="RP Financing $",
    y="FA Financing $",
    size="Total Financing $",
    color="Region",
    title="RP Financing vs FA Financing"
)
rp_vs_fa_scatter.update_layout(
    template="plotly_dark",
    font=dict(color="white"),
    title_font_size=20,
    paper_bgcolor="rgba(0, 0, 0, 0)",
)

# Layout
app.layout = html.Div([
    dcc.Tabs([
        dcc.Tab(label="Map & Distribution", children=[
            html.Div([
                html.P(
                    "RP Financing ($) represents readiness program funding, while FA Financing ($) represents funding approvals for projects.",
                    style={"color": "white", "fontSize": "16px"}
                ),
                html.Div([
                    dcc.Graph(figure=choropleth_map, style={"width": "48%", "display": "inline-block"}),
                    dcc.Graph(figure=financing_distribution_chart, style={"width": "48%", "display": "inline-block"})
                ])
            ])
        ]),
        dcc.Tab(label="SIDS and LDCs", children=[
            html.Div([
                html.P(
                    "SIDS (Small Island Developing States) and LDCs (Least Developed Countries) are vulnerable to climate change and require financial support to address these challenges.",
                    style={"color": "white", "fontSize": "16px"}
                ),
                dcc.Graph(figure=sids_ldcs_bar_chart)
            ])
        ]),
        dcc.Tab(label="RP vs FA Analysis", children=[
            html.Div([
                dcc.Graph(figure=rp_vs_fa_scatter)
            ])
        ]),
        dcc.Tab(label="Regional Analysis", children=[
            html.Div([
                dcc.Graph(figure=region_bar_chart)
            ])
        ])
    ])
], style={"backgroundColor": "black", "padding": "10px"})

if __name__ == '__main__':
    app.run_server(debug=True)