Gene Expression Levels by Cancer Type

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

np.random.seed(105)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'
COLORS = ['#9C2007', '#F5276C', '#27D3F5']

types = ['Subtype A', 'Subtype B', 'Subtype C']
data = pd.DataFrame({
    'Expression': np.concatenate([
        np.random.lognormal(2.5, 0.8, 90),
        np.random.lognormal(3.2, 0.6, 85),
        np.random.lognormal(2.8, 1.0, 95)
    ]),
    'Type': ['Subtype A']*90 + ['Subtype B']*85 + ['Subtype C']*95
})

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

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

ax.set_xlabel('Gene Expression (log2)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Cancer Subtype', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Biomarker Expression by Cancer Type', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#888', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#333')

plt.tight_layout()
plt.show()