Choropleth Map

import plotly.express as px
import pandas as pd
import numpy as np

# Generate sample GDP per capita data for demonstration
countries = [
    'United States', 'China', 'Japan', 'Germany', 'United Kingdom', 'France',
    'India', 'Italy', 'Brazil', 'Canada', 'Russia', 'South Korea', 'Spain',
    'Australia', 'Mexico', 'Indonesia', 'Netherlands', 'Saudi Arabia',
    'Turkey', 'Switzerland', 'Poland', 'Sweden', 'Belgium', 'Argentina',
    'Ireland', 'Norway', 'Israel', 'Austria', 'Nigeria', 'South Africa',
    'Egypt', 'United Arab Emirates', 'Thailand', 'Singapore', 'Malaysia',
    'Chile', 'Finland', 'Greece', 'Portugal', 'Czech Republic', 'Romania',
    'New Zealand', 'Vietnam', 'Bangladesh', 'Philippines', 'Pakistan',
    'Kazakhstan', 'Colombia', 'Peru', 'Ukraine', 'Cuba', 'Morocco',
    'Kenya', 'Ghana', 'Ethiopia', 'Tanzania', 'Uganda', 'Sudan',
    'Cameroon', 'Ivory Coast', 'Senegal', 'Zambia', 'Zimbabwe', 'Mozambique',
    'Angola', 'Botswana', 'Namibia', 'Madagascar', 'Malawi', 'Rwanda',
    'Burundi', 'Somalia', 'Libya', 'Tunisia', 'Algeria', 'Jordan',
    'Lebanon', 'Syria', 'Iraq', 'Yemen', 'Oman', 'Qatar', 'Kuwait',
    'Bahrain', 'Cyprus', 'Luxembourg', 'Monaco', 'Andorra', 'Malta',
    'Iceland', 'Estonia', 'Latvia', 'Lithuania', 'Slovakia', 'Slovenia',
    'Croatia', 'Bosnia', 'Serbia', 'Montenegro', 'Albania', 'Macedonia',
    'Bulgaria', 'Hungary', 'Denmark', 'Belarus', 'Moldova', 'Georgia',
    'Armenia', 'Azerbaijan', 'Uzbekistan', 'Turkmenistan', 'Kyrgyzstan',
    'Tajikistan', 'Mongolia', 'Nepal', 'Bhutan', 'Sri Lanka', 'Myanmar',
    'Laos', 'Cambodia', 'Papua New Guinea', 'Fiji', 'Solomon Islands',
    'Vanuatu', 'Samoa', 'Tonga', 'Kiribati', 'Marshall Islands', 'Palau'
]

# Generate realistic GDP per capita values (in USD)
np.random.seed(42)
gdp_values = []
for country in countries:
    # Assign GDP per capita based on economic regions
    if country in ['Luxembourg', 'Monaco', 'Switzerland', 'Ireland', 'Norway', 'Singapore']:
        gdp = np.random.uniform(60000, 120000)
    elif country in ['United States', 'Qatar', 'Kuwait', 'United Arab Emirates', 'Denmark', 'Netherlands', 'Sweden', 'Australia']:
        gdp = np.random.uniform(45000, 70000)
    elif country in ['Germany', 'Canada', 'United Kingdom', 'France', 'Japan', 'South Korea', 'New Zealand', 'Austria', 'Belgium']:
        gdp = np.random.uniform(35000, 50000)
    elif country in ['China', 'Russia', 'Brazil', 'Mexico', 'Turkey', 'Poland', 'Spain', 'Italy']:
        gdp = np.random.uniform(10000, 35000)
    elif country in ['India', 'Indonesia', 'Thailand', 'Malaysia', 'South Africa', 'Egypt', 'Nigeria']:
        gdp = np.random.uniform(2000, 10000)
    elif country in ['Pakistan', 'Bangladesh', 'Philippines', 'Vietnam', 'Kenya', 'Ghana', 'Ethiopia']:
        gdp = np.random.uniform(500, 3000)
    else:
        gdp = np.random.uniform(1000, 15000)
    gdp_values.append(gdp)

# Create DataFrame
df = pd.DataFrame({
    'Country': countries,
    'GDP_per_Capita': gdp_values
})

# Create choropleth map with logarithmic color scale
fig = px.choropleth(
    df,
    locations='Country',
    locationmode='country names',
    color='GDP_per_Capita',
    hover_name='Country',
    hover_data={'GDP_per_Capita': ':,.0f'},
    color_continuous_scale='Viridis',
    range_color=(100, 120000),
    title='World GDP per Capita (Logarithmic Scale)',
    labels={'GDP_per_Capita': 'GDP per Capita (USD)'},
    projection='natural earth'
)

# Update layout for better appearance
fig.update_layout(
    geo=dict(
        showframe=False,
        showcoastlines=True,
        coastlinecolor='LightGray',
        landcolor='White',
        oceancolor='LightBlue',
        lakecolor='LightBlue'
    ),
    margin=dict(l=0, r=0, t=50, b=0),
    height=600
)

# Add color bar with proper formatting and logarithmic scale
fig.update_coloraxes(
    colorbar=dict(
        title='GDP per Capita (USD)',
        tickformat=',.0f',
        thickness=20,
        len=0.7
    ),
    colorscale='Viridis',
    cmin=100,
    cmax=120000,
    colorbar_tickmode='array',
    colorbar_tickvals=[100, 1000, 10000, 100000],
    colorbar_ticktext=['$100', '$1,000', '$10,000', '$100,000']
)

# Show the figure
fig.show()

# Optional: Save the figure as an HTML file
# fig.write_html("world_gdp_per_capita.html")

# Optional: Save the figure as a static image (requires additional packages)
# fig.write_image("world_gdp_per_capita.png")

# Display summary statistics
print("\nGDP per Capita Summary Statistics:")
print(df['GDP_per_Capita'].describe())

# Display top 10 countries by GDP per capita
print("\nTop 10 Countries by GDP per Capita:")
top_10 = df.nlargest(10, 'GDP_per_Capita')
for idx, row in top_10.iterrows():
    print(f"{row['Country']}: ${row['GDP_per_Capita']:,.0f}")

# Display bottom 10 countries by GDP per capita
print("\nBottom 10 Countries by GDP per Capita:")
bottom_10 = df.nsmallest(10, 'GDP_per_Capita')
for idx, row in bottom_10.iterrows():
    print(f"{row['Country']}: ${row['GDP_per_Capita']:,.0f}")
fig.show()
# END-OF-CODE