Delivery Time Cumulative KDE

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

np.random.seed(777)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'
GRID_COLOR = '#e5e7eb'

# Delivery time in days
express = np.random.gamma(2, 0.5, 500) + 1
standard = np.random.gamma(3, 1.2, 500) + 2
economy = np.random.gamma(4, 1.5, 500) + 3

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

x_range = np.linspace(0, 20, 500)

for data, color, label in [(express, '#F5276C', 'Express'), 
                            (standard, '#4927F5', 'Standard'),
                            (economy, '#F5B027', 'Economy')]:
    kde = gaussian_kde(data)
    cdf = np.array([kde.integrate_box_1d(-np.inf, x) for x in x_range])
    ax.plot(x_range, cdf, color=color, linewidth=3, label=label)

ax.axhline(0.95, color='#22c55e', linestyle='--', alpha=0.7, linewidth=2)
ax.text(15, 0.96, '95% SLA', color='#22c55e', fontsize=10)

ax.set_xlabel('Delivery Time (days)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Cumulative Probability', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Delivery Time: Cumulative Distribution', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors=TEXT_COLOR, labelsize=10)
for spine in ax.spines.values():
    spine.set_color(GRID_COLOR)
ax.yaxis.grid(True, color=GRID_COLOR, linewidth=0.5, alpha=0.7)
ax.legend(facecolor=BG_COLOR, edgecolor=GRID_COLOR, labelcolor=TEXT_COLOR, fontsize=10, loc='lower right')

plt.tight_layout()
plt.show()