Student Test Scores Distribution

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

np.random.seed(45)

# Generate test scores with different performance groups
low_performers = np.random.normal(55, 10, 150)
average = np.random.normal(72, 8, 400)
high_performers = np.random.normal(90, 5, 200)
scores = np.concatenate([low_performers, average, high_performers])
scores = np.clip(scores, 0, 100)

kde = stats.gaussian_kde(scores)
x = np.linspace(0, 100, 500)
y = kde(x)

colors = ['#1e3a8a', '#3b82f6', '#22d3ee', '#fbbf24', '#f97316', '#dc2626']

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

# Color based on score (low=cool, high=warm)
for i in range(len(x)-1):
    score_normalized = x[i] / 100
    color_idx = int(score_normalized * (len(colors) - 1))
    ax.fill_between(x[i:i+2], y[i:i+2], alpha=0.7, color=colors[color_idx])

ax.plot(x, y, color='white', linewidth=2.5)

# Grade thresholds
grades = [(90, 'A'), (80, 'B'), (70, 'C'), (60, 'D')]
for threshold, grade in grades:
    ax.axvline(threshold, color='#e2e8f0', linestyle=':', alpha=0.5, linewidth=1)
    ax.text(threshold-2, max(y)*0.9, grade, color='#e2e8f0', fontsize=10, fontweight='bold')

mean_score = np.mean(scores)
ax.axvline(mean_score, color='#fbbf24', linestyle='--', linewidth=2)
ax.text(mean_score+2, max(y)*0.8, f'Mean: {mean_score:.1f}', color='#fbbf24', fontsize=10, fontweight='bold')

ax.set_xlabel('Score (%)', fontsize=12, color='white', fontweight='500')
ax.set_ylabel('Density', fontsize=12, color='white', fontweight='500')
ax.set_title('Student Test Scores 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.set_xlim(0, 100)
ax.grid(True, alpha=0.1, color='white')

plt.tight_layout()
plt.show()