Income Distribution Analysis

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

np.random.seed(42)

income1 = np.random.normal(45000, 15000, 500)
income2 = np.random.normal(85000, 20000, 300)
income = np.concatenate([income1, income2])
income = income[income > 0]

kde = stats.gaussian_kde(income)
x = np.linspace(0, 150000, 500)
y = kde(x)

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

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

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, alpha=0.9)

for p, label in [(25, '25th'), (50, 'Median'), (75, '75th')]:
    val = np.percentile(income, p)
    ax.axvline(val, color='#fbbf24', linestyle='--', alpha=0.6, linewidth=1.5)
    val_k = val/1000
    ax.text(val, max(y)*0.95, label + ' $' + str(int(val_k)) + 'K', color='#fbbf24', 
            fontsize=9, ha='center', fontweight='bold')

ax.set_xlabel('Annual Income ($)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Household Income Distribution', fontsize=16, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='white', labelsize=10)
ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: '$' + str(int(x/1000)) + 'K'))
for spine in ax.spines.values():
    spine.set_color('#334155')
ax.set_xlim(0, 150000)
ax.set_ylim(0, None)
ax.grid(True, alpha=0.1, color='white')

plt.tight_layout()
plt.show()