Cohort Retention Heatmap

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

fig, ax = plt.subplots(figsize=(14, 8), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

np.random.seed(42)
cohorts = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
periods = ['M0', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6']
data = np.zeros((len(cohorts), len(periods)))
for i in range(len(cohorts)):
    data[i, 0] = 100
    for j in range(1, len(periods)):
        data[i, j] = data[i, j-1] * np.random.uniform(0.7, 0.95)

colors = ['#1e293b', '#0c4a6e', '#0284c7', '#38bdf8', '#bae6fd']
cmap = LinearSegmentedColormap.from_list('modern', colors, N=256)

cell_w, cell_h = 0.88, 0.82
for i in range(len(cohorts)):
    for j in range(len(periods)):
        val = data[i, j]
        rect = FancyBboxPatch((j - cell_w/2, i - cell_h/2), cell_w, cell_h,
                               boxstyle="round,pad=0.02,rounding_size=0.12",
                               facecolor=cmap((val - 0) / (100 - 0)), edgecolor='#e2e8f0', linewidth=1.5)
        ax.add_patch(rect)
        ax.text(j, i, f'{val:.0f}%', ha='center', va='center', color='#1e293b', fontsize=10, fontweight='bold')

ax.set_xlim(-0.5, len(periods)-0.5)
ax.set_ylim(-0.5, len(cohorts)-0.5)
ax.set_aspect('equal')
ax.invert_yaxis()
ax.set_xticks(range(len(periods)))
ax.set_yticks(range(len(cohorts)))
ax.set_xticklabels(periods, color='#64748b', fontsize=10, fontweight='500')
ax.set_yticklabels(cohorts, color='#1e293b', fontsize=11, fontweight='500')

sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(0, 100))
cbar = plt.colorbar(sm, ax=ax, shrink=0.8, pad=0.02)
cbar.set_label('Retention %', color='#1e293b', fontsize=11)
cbar.outline.set_edgecolor('#e2e8f0')
plt.setp(plt.getp(cbar.ax.axes, 'yticklabels'), color='#64748b')

for spine in ax.spines.values(): spine.set_visible(False)
ax.tick_params(length=0)
ax.set_title('Cohort Retention Analysis', fontsize=18, color='#1e293b', fontweight='bold', pad=20)
plt.tight_layout()
plt.show()