Product Rating Distribution

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

np.random.seed(55)

# Ratings tend to be J-shaped (more high ratings)
ratings = np.concatenate([
    np.random.normal(4.3, 0.5, 600),
    np.random.normal(2.5, 0.8, 150),
    np.random.uniform(1, 5, 250)
])
ratings = np.clip(ratings, 1, 5)

kde = stats.gaussian_kde(ratings)
x = np.linspace(1, 5, 500)
y = kde(x)

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

# Sentiment-based coloring
for i in range(len(x)-1):
    if x[i] < 2:
        color = '#F5276C'  # Negative
    elif x[i] < 3:
        color = '#F54927'  # Mixed negative
    elif x[i] < 4:
        color = '#F5B027'  # Neutral
    else:
        color = '#6CF527'  # Positive
    ax.fill_between(x[i:i+2], y[i:i+2], alpha=0.6, color=color)

ax.plot(x, y, color='#F5B027', linewidth=3)
ax.plot(x, y, color='#F5B027', linewidth=8, alpha=0.2)

# Mean line
mean_rating = np.mean(ratings)
ax.axvline(mean_rating, color='white', linestyle='--', linewidth=2)
ax.text(mean_rating+0.1, max(y)*0.85, f'Avg: {mean_rating:.2f}', color='white', fontsize=11, fontweight='bold')

ax.set_xlabel('Rating (1-5 stars)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Product Rating 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.grid(True, alpha=0.1, color='white')
ax.set_xlim(1, 5)

plt.tight_layout()
plt.show()