Energy Consumption by Building Type

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import ptitprince as pt

np.random.seed(219)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'
COLORS = ['#F5B027', '#27D3F5', '#6CF527', '#F5276C']

buildings = ['Office', 'Retail', 'Warehouse', 'Hospital']
data = pd.DataFrame({
    'kWh': np.concatenate([
        np.random.lognormal(4.5, 0.5, 80),
        np.random.lognormal(4.2, 0.4, 90),
        np.random.lognormal(3.8, 0.6, 70),
        np.random.lognormal(5.0, 0.45, 60)
    ]),
    'Building': ['Office']*80 + ['Retail']*90 + ['Warehouse']*70 + ['Hospital']*60
})

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

pt.RainCloud(x='Building', y='kWh', data=data, palette=COLORS,
             bw=.2, width_viol=.6, ax=ax, orient='h', alpha=.65,
             dodge=True, pointplot=False, move=.2)

ax.set_xlabel('Energy (kWh/sqft/year)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Building Type', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Energy Consumption by Building Type', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#374151', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#e5e7eb')

plt.tight_layout()
plt.show()