Bivariate Density Analysis

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)
x = np.random.randn(8000)
y = 0.8 * x + np.random.randn(8000) / 2

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

colors = ['#0a0a0f', '#1a1a3a', '#2d1f5a', '#4a1a7a', '#6b2d9a', '#8b3fba', '#ab5fda', '#cb8ffa']
cmap = LinearSegmentedColormap.from_list('purple_glow', colors, N=256)

hb = ax.hexbin(x, y, gridsize=30, cmap=cmap, mincnt=1, edgecolors='none')

cbar = plt.colorbar(hb, ax=ax, pad=0.02, shrink=0.8)
cbar.ax.set_facecolor('#0a0a0f')
cbar.outline.set_edgecolor('#2a2a4a')
cbar.ax.tick_params(colors='#8b8fba', labelsize=9)
cbar.set_label('Count', color='#8b8fba', fontsize=10)

ax.set_xlabel('X Variable', color='#8b8fba', fontsize=11)
ax.set_ylabel('Y Variable', color='#8b8fba', fontsize=11)
ax.tick_params(colors='#8b8fba', labelsize=10)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()