World Health Indicators

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)

fig, ax = plt.subplots(figsize=(14, 9), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

countries = ['Japan', 'USA', 'Germany', 'Brazil', 'India', 'Nigeria', 'Australia', 'China']
life_exp = np.array([84.5, 78.9, 81.3, 75.9, 70.4, 54.7, 83.4, 78.2])
gdp_pc = np.array([42, 76, 51, 9, 2.4, 2.2, 60, 13])
population = np.array([125, 335, 84, 216, 1420, 220, 26, 1410])

colors = ['#F54927', '#276CF5', '#F5B027', '#6CF527', '#F5276C', '#27D3F5', '#4927F5', '#C82909']
sizes = population / 2

# Soft shadow effect for light theme
for glow_mult, glow_alpha in [(2.0, 0.03), (1.6, 0.05), (1.3, 0.08)]:
    ax.scatter(gdp_pc, life_exp, s=sizes*glow_mult, c='#000000', alpha=glow_alpha, edgecolors='none')

ax.scatter(gdp_pc, life_exp, s=sizes, c=colors, alpha=0.85, edgecolors='white', linewidth=2)
ax.scatter(gdp_pc - np.sqrt(sizes)*0.08, life_exp + np.sqrt(sizes)*0.012, s=sizes*0.2, c='white', alpha=0.6, edgecolors='none')

for i, country in enumerate(countries):
    offset_y = np.sqrt(sizes[i])/2 + 12
    ax.annotate(country, (gdp_pc[i], life_exp[i]), fontsize=11, color='#1f2937',
                ha='center', va='bottom', xytext=(0, offset_y), textcoords='offset points', fontweight='bold')

ax.text(0.0, 1.08, 'World Health Indicators', transform=ax.transAxes, fontsize=24, color='#1f2937', fontweight='bold')
ax.text(0.0, 1.02, 'GDP per Capita vs Life Expectancy · Bubble size = Population', transform=ax.transAxes, fontsize=11, color='#6b7280')

ax.set_xlabel('GDP per Capita ($K)', fontsize=14, color='#4b5563', fontweight='500', labelpad=15)
ax.set_ylabel('Life Expectancy (Years)', fontsize=14, color='#4b5563', fontweight='500', labelpad=15)
ax.tick_params(colors='#6b7280', labelsize=11, length=0)

for y in [50, 60, 70, 80, 90]:
    ax.axhline(y=y, color='#f3f4f6', linewidth=1, zorder=0)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()