Nutrient Composition 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(333)

nutrients = ['Protein', 'Carbs', 'Fat', 'Fiber', 'Vitamin A', 'Vitamin C',
             'Calcium', 'Iron', 'Potassium', 'Sodium']
foods = ['Salmon', 'Chicken', 'Beef', 'Spinach', 'Broccoli', 'Banana', 
         'Rice', 'Quinoa', 'Almonds', 'Eggs', 'Milk', 'Tofu']

data = np.random.rand(10, 12) * 30
data[0, :3] += 40
data[0, 9:11] += 25
data[4:6, 3:5] += 50
data[1, 6:8] += 40

df = pd.DataFrame(data, index=nutrients, columns=foods)

groups = ['Meat']*3 + ['Vegetable']*2 + ['Fruit'] + ['Grain']*2 + ['Nut'] + ['Animal']*3
group_palette = {'Meat': '#ef4444', 'Vegetable': '#22c55e', 'Fruit': '#f59e0b', 
                 'Grain': '#f97316', 'Nut': '#a855f7', 'Animal': '#3b82f6'}
col_colors = pd.Series(groups, index=foods).map(group_palette)

light_cmap = LinearSegmentedColormap.from_list('nutrient', ['#f8fafc', '#bfdbfe', '#3b82f6', '#1e40af'])

g = sns.clustermap(df, cmap=light_cmap, col_colors=col_colors,
                   method='average', metric='euclidean', standard_scale=0,
                   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('Food Nutrient Profiles', color='#1f2937', fontsize=13, fontweight='bold', y=1.02)
plt.show()