Website Load Time Distribution

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

np.random.seed(54)

# Load times (log-normal typical for web performance)
load_times = np.random.lognormal(0.8, 0.6, 2000)
load_times = load_times[load_times < 15]

kde = stats.gaussian_kde(load_times)
x = np.linspace(0, 10, 500)
y = kde(x)

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

# Color gradient based on performance
for i in range(len(x)-1):
    if x[i] < 2:
        color = '#6CF527'  # Fast - lime
    elif x[i] < 4:
        color = '#27D3F5'  # Good - cyan
    elif x[i] < 6:
        color = '#F5B027'  # Slow - amber
    else:
        color = '#F5276C'  # Very slow - coral
    ax.fill_between(x[i:i+2], y[i:i+2], alpha=0.6, color=color)

ax.plot(x, y, color='#276CF5', linewidth=3)
ax.plot(x, y, color='#276CF5', linewidth=8, alpha=0.2)

# Benchmarks
benchmarks = [(2, 'Good'), (4, 'Needs Work'), (6, 'Poor')]
for val, label in benchmarks:
    ax.axvline(val, color='#e2e8f0', linestyle='--', linewidth=1.5, alpha=0.7)
    ax.text(val+0.1, max(y)*0.9, label, color='#e2e8f0', fontsize=9, fontweight='bold')

# Core Web Vitals LCP
ax.axvline(2.5, color='#F5B027', linestyle='-', linewidth=2, label='LCP Target (2.5s)')

ax.set_xlabel('Load Time (seconds)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Website Load Time 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.legend(loc='upper right', facecolor='#1e293b', edgecolor='#334155', labelcolor='white')
ax.grid(True, alpha=0.1, color='white')
ax.set_xlim(0, 10)

plt.tight_layout()
plt.show()