Male vs Female Salary Distribution

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

np.random.seed(42)
BG_COLOR = '#0a0a0f'

# Salary data (thousands)
male = np.random.normal(78, 18, 1200)
female = np.random.normal(65, 16, 1200)

fig, ax = plt.subplots(figsize=(12, 7), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

x = np.linspace(15, 140, 500)

# Male (top) with glow effect
kde_m = gaussian_kde(male)
y_m = kde_m(x)
ax.fill_between(x, y_m, alpha=0.15, color='#27D3F5')
ax.fill_between(x, y_m, alpha=0.08, color='#27D3F5')
ax.plot(x, y_m, color='#27D3F5', linewidth=4, alpha=0.3)
ax.plot(x, y_m, color='#27D3F5', linewidth=2, label=f'Male (μ=${np.mean(male):.0f}K)')

# Female (bottom) with glow effect
kde_f = gaussian_kde(female)
y_f = kde_f(x) * -1
ax.fill_between(x, y_f, alpha=0.15, color='#F5276C')
ax.fill_between(x, y_f, alpha=0.08, color='#F5276C')
ax.plot(x, y_f, color='#F5276C', linewidth=4, alpha=0.3)
ax.plot(x, y_f, color='#F5276C', linewidth=2, label=f'Female (μ=${np.mean(female):.0f}K)')

# Center axis
ax.axhline(0, color='#444444', linewidth=1.5)

# Mean lines with annotations
m_mean, f_mean = np.mean(male), np.mean(female)
ax.axvline(m_mean, color='#27D3F5', linestyle='--', alpha=0.5, linewidth=1.5)
ax.axvline(f_mean, color='#F5276C', linestyle='--', alpha=0.5, linewidth=1.5)

# Gap annotation
gap = ((m_mean - f_mean) / f_mean) * 100
ax.annotate('', xy=(m_mean, max(y_m)*0.7), xytext=(f_mean, max(y_m)*0.7),
            arrowprops=dict(arrowstyle='<->', color='#F5B027', lw=2))
ax.text((m_mean + f_mean)/2, max(y_m)*0.75, f'Gap: {gap:.1f}%', 
        ha='center', fontsize=11, color='#F5B027', fontweight='bold')

# Stats box
stats = f"Δμ = ${m_mean-f_mean:.1f}K | Male σ={np.std(male):.1f} | Female σ={np.std(female):.1f}"
ax.text(0.5, 0.98, stats, transform=ax.transAxes, ha='center', va='top',
        fontsize=10, color='#888888', fontfamily='monospace',
        bbox=dict(boxstyle='round,pad=0.4', facecolor=BG_COLOR, edgecolor='#333333'))

ax.set_xlabel('Annual Salary ($K)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Gender Pay Gap Analysis', fontsize=16, color='white', fontweight='bold', pad=20)

ax.tick_params(colors='#888888', labelsize=10)
ax.set_yticks([])
for spine in ['top', 'right']:
    ax.spines[spine].set_visible(False)
for spine in ['bottom', 'left']:
    ax.spines[spine].set_color('#333333')
ax.legend(loc='upper right', facecolor=BG_COLOR, edgecolor='#333333', 
          labelcolor='white', fontsize=10, framealpha=0.9)

plt.tight_layout()
plt.show()