Ridgeline Plot
Delivery Time by Carrier
Shipping speed distributions with modern gradient aesthetics
Output
Python
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
np.random.seed(654)
carriers = ['FedEx', 'UPS', 'USPS', 'DHL', 'Amazon', 'OnTrac']
colors = ['#F54927', '#4927F5', '#276CF5', '#F5D327', '#F5B027', '#6CF527']
data = {
'FedEx': np.random.gamma(2, 0.8, 500),
'UPS': np.random.gamma(2.2, 0.9, 500),
'USPS': np.random.gamma(3, 1.2, 500),
'DHL': np.random.gamma(2.5, 1, 500),
'Amazon': np.random.gamma(1.5, 0.7, 500),
'OnTrac': np.random.gamma(2.8, 1.1, 500)
}
fig, ax = plt.subplots(figsize=(12, 8), facecolor='#ffffff')
ax.set_facecolor('#ffffff')
overlap = 1.6
x_range = np.linspace(0, 10, 300)
for i, (carrier, days) in enumerate(data.items()):
kde = stats.gaussian_kde(days, bw_method=0.3)
y = kde(x_range) * 3.5
baseline = i * overlap
ax.fill_between(x_range, baseline, y + baseline, alpha=0.7, color=colors[i])
ax.plot(x_range, y + baseline, color=colors[i], linewidth=2.5)
ax.text(-0.3, baseline + 0.12, carrier, fontsize=11, color='#1f2937',
ha='right', va='bottom', fontweight='600')
ax.set_xlim(-2, 10)
ax.set_ylim(-0.3, len(carriers) * overlap + 1.8)
ax.set_xlabel('Days to Delivery', fontsize=12, color='#374151', fontweight='500')
ax.set_title('Delivery Time by Carrier', fontsize=16, color='#1f2937', fontweight='bold', pad=20)
ax.tick_params(axis='x', colors='#374151', labelsize=10)
ax.tick_params(axis='y', left=False, labelleft=False)
ax.spines['bottom'].set_color('#e5e7eb')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
plt.tight_layout()
plt.show()
Library
Matplotlib
Category
Statistical
More Ridgeline Plot examples
☕