Population Age Distribution

import matplotlib.pyplot as plt
import numpy as np
from scipy import stats

np.random.seed(43)

# Generate realistic age distribution
ages = np.concatenate([
    np.random.normal(25, 8, 400),
    np.random.normal(45, 12, 500),
    np.random.normal(70, 10, 300)
])
ages = ages[(ages >= 0) & (ages <= 100)]

kde = stats.gaussian_kde(ages)
x = np.linspace(0, 100, 500)
y = kde(x)

colors = ['#1e3a8a', '#3b82f6', '#22d3ee', '#fbbf24', '#f97316', '#dc2626']

fig, ax = plt.subplots(figsize=(12, 6), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

# Gradient fill based on density
for i in range(len(x)-1):
    color_idx = int((y[i] / max(y)) * (len(colors) - 1))
    ax.fill_between(x[i:i+2], y[i:i+2], alpha=0.7, color=colors[min(color_idx, len(colors)-1)])

ax.plot(x, y, color='white', linewidth=2.5)

# Age group annotations
ax.axvspan(0, 18, alpha=0.1, color='#3b82f6', label='Youth')
ax.axvspan(18, 65, alpha=0.1, color='#22d3ee', label='Working Age')
ax.axvspan(65, 100, alpha=0.1, color='#f97316', label='Senior')

ax.set_xlabel('Age (Years)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Population Age Distribution', fontsize=16, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='white', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#334155')
ax.set_xlim(0, 100)
ax.legend(loc='upper right', facecolor='#1e293b', edgecolor='#334155', labelcolor='white')
ax.grid(True, alpha=0.1, color='white')

plt.tight_layout()
plt.show()