Lorenz Curves: A global and national perspectives

economy
python
The charts show the distribution of income and wealth among countries and globally.
Published

May 18, 2025

Keywords

lorenz curves

Summary

This is a global graphical representation of income and wealth for the years 1995 and 2021. Illustrating the dynamics of inequality by comparing Gini indices through Lorenz curves, both within (intra-country) and between (inter-country) nations.

Code 
# Libraries
# ===================================================
import requests
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib.patheffects as patheffects

# Parameters
# =====================================================================
# Select between Income/Wealth
selection = 'Income'
year = 2021

# Data Extraction (Countries)
# =====================================================================
# Extract JSON and bring data to a dataframe
url = 'https://raw.githubusercontent.com/guillemmaya92/world_map/main/Dim_Country.json'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data)
df = pd.DataFrame.from_dict(data, orient='index').reset_index()
df_countries = df.rename(columns={'index': 'ISO3'})

# Data Extraction (Percentages)
# ===================================================
# URL GitHub
url = "https://raw.githubusercontent.com/guillemmaya92/Python/main/Data/WID_Percentiles.parquet"

# Extract data from parquet
df = pd.read_parquet(url, engine='pyarrow')

# Filter year
df = df[df['year'] == year]

# Data Extraction (Values)
# ===================================================
# URL GitHub
url = "https://raw.githubusercontent.com/guillemmaya92/Python/main/Data/WID_Values.parquet"

# Extract data from parquet
dfv = pd.read_parquet(url, engine='pyarrow')

# Filter year
dfv = dfv[dfv['year'] == year]

# Extract world values
gincomew = dfv.loc[dfv['country'] == 'WO', 'gincome'].iloc[0]
gwealthw = dfv.loc[dfv['country'] == 'WO', 'gwealth'].iloc[0]

# Extract countries weighted average values
dfincome = dfv[dfv['country'].isin(df_countries['ISO2']) & dfv['gincome'].notnull() & dfv['population'].notnull()]
dfwealth = dfv[dfv['country'].isin(df_countries['ISO2']) & dfv['gwealth'].notnull() & dfv['population'].notnull()]

gincomec = np.average(dfincome['gincome'], weights= dfincome['population'])
gwealthc = np.average(dfwealth['gwealth'], weights= dfwealth['population'])

# Dynamic value
giniw = round(gwealthw, 2) if selection == 'Wealth' else round(gincomew, 2)
ginic= round(gwealthc, 2) if selection == 'Wealth' else round(gincomec, 2)

# Data Manipulation
# ===================================================
# Calculate cummulative
df['percentile'] =  df['percentile'] / 100
df['income'] =  df['income'] / 100
df['wealth'] =  df['wealth'] / 100
df['income_cum'] =  df.groupby(['country'])['income'].cumsum() / df.groupby(['country'])['income'].transform('sum')
df['wealth_cum'] =  df.groupby(['country'])['wealth'].cumsum() / df.groupby(['country'])['wealth'].transform('sum')
df['value_cum'] = df['income_cum'] if selection == 'Income' else df['wealth_cum']

# Countries
dfc = df.merge(df_countries, how='left', left_on='country', right_on='ISO2')
dfc = dfc[dfc['Region'].notna()]
dfc = dfc[['country', 'percentile', 'value_cum']]

# World
dfw = df[df['country'] == "WO"]
dfw = dfw[['country', 'percentile', 'value_cum']]
dfw['country'] = 'Inter-Countries'

print(df)

# Data Visualization
# ===================================================
# Font Style
plt.rcParams.update({'font.family': 'sans-serif', 'font.sans-serif': ['Open Sans'], 'font.size': 10})
plt.figure(figsize=(10, 10))

# Basic Grey Plot Lines
sns.lineplot(
    data=dfc, 
    x="percentile", 
    y="value_cum", 
    hue="country",
    linewidth=0.4,
    alpha=0.5,
    palette=['#808080']
).legend_.remove()

# Black Shadow Plot Lines
sns.lineplot(
    data=dfw, 
    x="percentile", 
    y="value_cum", 
    hue="country",
    linewidth=2.25,
    alpha=1,
    palette=['black']
).legend_.remove()

# Color Plot Lines
sns.lineplot(
    data=dfw, 
    x="percentile", 
    y="value_cum", 
    hue="country",
    linewidth=1.5,
    alpha=1,
    palette=['#FF0000']
).legend_.remove()

# Add Inequality lines
plt.plot([0, 1], [0, 1], color="gray", linestyle="-", linewidth=1)

# Configuración del gráfico
plt.text(0, 1.05, f'Global {selection} Distribution', fontsize=13, fontweight='bold', ha='left', transform=plt.gca().transAxes)
plt.text(0, 1.02, 'A global and national perspective on Lorenz curves', fontsize=9, color='#262626', ha='left', transform=plt.gca().transAxes)
plt.xlabel('Cumulative Population (%)', fontsize=10, fontweight='bold')
plt.ylabel(f'Cumulative {selection} (%)', fontsize=10, fontweight='bold')
plt.xlim(0, 1)
plt.ylim(0, 1)

# Adjust grid and layout
plt.grid(True, linestyle='-', color='grey', linewidth=0.08)
plt.gca().set_aspect('equal', adjustable='box')

# Add Data Source
plt.text(0, -0.1, 'Data Source: World Inequality Database (WID)', 
    transform=plt.gca().transAxes, 
    fontsize=8,
    fontweight='bold',
    color='gray')

# Variable notes
noteincome = 'Income: Post-tax national income is the sum of primary incomes over all sectors (private and public), minus taxes.'
notewealth = 'Wealth: Total value of non-financial and financial assets held by households, minus their debts.'
note = noteincome if selection == 'income' else notewealth

# Add Notes
plt.text(0, -0.12, note, 
    transform=plt.gca().transAxes, 
    fontsize=7,
    fontstyle='italic',
    color='gray')

# Add Author
plt.text(0.85, -0.1, '@guillemmaya.com', 
    transform=plt.gca().transAxes, 
    fontsize=9,
    fontstyle='italic',
    color='#212121')

 # Add Year label
formatted_date = year
plt.text(1, 1.06, f'{formatted_date}',
    transform=plt.gca().transAxes,
    fontsize=22, ha='right', va='top',
    fontweight='bold', color='#D3D3D3')

# Create custom lines
intra_line = mlines.Line2D([], [], color='#808080', label=f'Gini Intra-Countries: {ginic}', linewidth=2)
inter_line = mlines.Line2D([], [], color='#FF0000', label=f'Gini Inter-Countries: {giniw}', linewidth=2)
inter_line.set_path_effects([patheffects.withStroke(linewidth=4, foreground='black')])
inter_circle = mlines.Line2D([], [], marker='o', color='w', markerfacecolor='#FF0000', markeredgecolor='black', markersize=8, label='Inter-Countries', linewidth=0)

# Add custom legend
plt.legend(handles=[intra_line, inter_line])

# Save the figure
plt.savefig('C:/Users/guill/Desktop/FIG_WID_Global_Lorenz_Curves.png', format='png', dpi=300, bbox_inches='tight')

# Show the plot!
plt.show()

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