Coffee Consumption vs Productivity

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

np.random.seed(42)

# Productivity study
coffee = np.clip(np.random.normal(3, 1.5, 4000), 0, 8)
productivity = 50 + coffee * 8 - (coffee - 3)**2 * 2 + np.random.normal(0, 10, 4000)
productivity = np.clip(productivity, 20, 100)

fig, ax = plt.subplots(figsize=(10, 8), facecolor='#020B14')
ax.set_facecolor('#020B14')

# Custom colormap: dark brown to amber
colors = ['#020B14', '#2d1a0d', '#9C2007', '#F5B027']
cmap = LinearSegmentedColormap.from_list('coffee', colors, N=256)

h = ax.hist2d(coffee, productivity, bins=40, cmap=cmap, cmin=1)
cbar = plt.colorbar(h[3], ax=ax, pad=0.02)
cbar.set_label('Days', color='white', fontsize=11)
cbar.ax.yaxis.set_tick_params(color='white')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='white')

ax.set_xlabel('Coffee Cups/Day', fontsize=11, color='white', fontweight='500')
ax.set_ylabel('Productivity Score', fontsize=11, color='white', fontweight='500')
ax.set_title('Coffee Consumption vs Productivity', fontsize=14, color='white', fontweight='bold', pad=15)

ax.tick_params(colors='white', labelsize=9)
for spine in ax.spines.values():
    spine.set_color('#333333')

plt.tight_layout()
plt.show()