Crypto Market Correlations

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(321)

cryptos = ['BTC', 'ETH', 'BNB', 'SOL', 'ADA', 'XRP', 'DOT', 'AVAX', 'MATIC', 'LINK']
n = len(cryptos)

corr = np.eye(n)
corr[0, 1] = corr[1, 0] = 0.85
corr[0, 2] = corr[2, 0] = 0.75
corr[1, 3] = corr[3, 1] = 0.70
corr[3, 7] = corr[7, 3] = 0.65
corr[6, 7] = corr[7, 6] = 0.60
corr[8, 9] = corr[9, 8] = 0.55

for i in range(n):
    for j in range(i+1, n):
        if corr[i, j] == 0:
            corr[i, j] = corr[j, i] = np.random.uniform(0.3, 0.6)

df = pd.DataFrame(corr, index=cryptos, columns=cryptos)

neon_cmap = LinearSegmentedColormap.from_list('crypto', ['#0a0a0f', '#4927F5', '#27D3F5', '#6CF527', '#F5B027'])

g = sns.clustermap(df, cmap=neon_cmap, vmin=0, vmax=1,
                   method='average', linewidths=0.5, linecolor='#1a1a2e',
                   figsize=(8, 7), dendrogram_ratio=(0.12, 0.12),
                   annot=True, fmt='.2f', annot_kws={'size': 8, 'color': 'white'},
                   cbar_pos=(0.01, 0.08, 0.008, 0.12),
                   tree_kws={'linewidths': 1.5, 'colors': '#F5B027'})

g.fig.patch.set_facecolor('#0a0a0f')
g.ax_heatmap.set_facecolor('#0a0a0f')
g.ax_heatmap.tick_params(colors='white', labelsize=9)
g.ax_row_dendrogram.set_facecolor('#0a0a0f')
g.ax_col_dendrogram.set_facecolor('#0a0a0f')
g.cax.tick_params(colors='white', labelsize=7)

g.fig.suptitle('Cryptocurrency Correlations', color='white', fontsize=13, fontweight='bold', y=1.02)
plt.show()