Student Performance Matrix

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(444)

subjects = ['Math', 'Physics', 'Chemistry', 'Biology', 'English', 
            'History', 'Geography', 'Art', 'Music', 'PE']
students = [f'Student_{i}' for i in range(1, 16)]

data = np.random.rand(10, 15) * 40 + 50
data[:4, :5] += 20
data[4:7, 5:10] += 15
data[7:9, 10:] += 25

df = pd.DataFrame(data, index=subjects, columns=students)

types = ['STEM']*5 + ['Humanities']*5 + ['Arts']*5
type_palette = {'STEM': '#3b82f6', 'Humanities': '#8b5cf6', 'Arts': '#ec4899'}
col_colors = pd.Series(types, index=students).map(type_palette)

light_cmap = LinearSegmentedColormap.from_list('grades', ['#fef2f2', '#fef9c3', '#dcfce7', '#22c55e'])

g = sns.clustermap(df, cmap=light_cmap, vmin=40, vmax=100, col_colors=col_colors,
                   method='ward', metric='euclidean',
                   linewidths=0.5, linecolor='#e5e7eb',
                   figsize=(10, 8), dendrogram_ratio=(0.12, 0.12),
                   cbar_pos=(0.01, 0.08, 0.008, 0.12))

g.fig.patch.set_facecolor('#ffffff')
g.ax_heatmap.set_facecolor('#ffffff')
g.ax_heatmap.tick_params(colors='#1f2937', labelsize=8)
g.ax_row_dendrogram.set_facecolor('#ffffff')
g.ax_col_dendrogram.set_facecolor('#ffffff')
g.cax.tick_params(colors='#1f2937', labelsize=7)

g.fig.suptitle('Student Academic Performance', color='#1f2937', fontsize=13, fontweight='bold', y=1.02)
plt.show()