تصور رحلات المستخدمين

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Assume funnel_summary and retention_pct are already computed
print('Visualisation libraries loaded')

FUNNEL_STEPS = ['homepage', 'product_page', 'add_to_cart', 'checkout', 'purchase']
users = [10000, 7200, 4100, 2300, 980]

fig, ax = plt.subplots(figsize=(9, 5))
bars = ax.barh(FUNNEL_STEPS[::-1], users[::-1], color='steelblue', height=0.6)
ax.set_xlabel('Unique Users')
ax.set_title('Conversion Funnel')
plt.tight_layout()
plt.show()

conversions = [None, 72.0, 56.9, 56.1, 42.6]

for i, (u, c) in enumerate(zip(users[::-1], conversions[::-1])):
    label = f'{u:,} users'
    if c is not None:
        label += f'  ({c}% conv)'
    ax.text(u + 100, i, label, va='center', fontsize=9)

plt.tight_layout()
plt.show()

import numpy as np

# Simulated retention data for illustration
data = [[100, 55, 42, 38, 35, 33],
        [100, 52, 40, 36, 33, 0],
        [100, 58, 45, 41, 0, 0],
        [100, 60, 48, 0, 0, 0]]
retention_pct = pd.DataFrame(data, columns=range(6),
                             index=['Wk1', 'Wk2', 'Wk3', 'Wk4'])

fig, ax = plt.subplots(figsize=(10, 4))
sns.heatmap(retention_pct, annot=True, fmt='.0f', cmap='RdYlGn',
            vmin=0, vmax=100, linewidths=0.5, ax=ax)
ax.set_title('Cohort Retention Heatmap')
plt.tight_layout()
plt.show()

mask = retention_pct == 0  # cells with no data yet

fig, ax = plt.subplots(figsize=(10, 4))
sns.heatmap(retention_pct, annot=True, fmt='.0f', cmap='RdYlGn',
            vmin=0, vmax=100, linewidths=0.5,
            mask=mask, ax=ax)
ax.set_title('Cohort Retention (incomplete cells masked)')
plt.tight_layout()
plt.show()

avg = retention_pct.mean(axis=0)
std = retention_pct.std(axis=0)

fig, ax = plt.subplots(figsize=(8, 4))
ax.plot(avg.index, avg, marker='o', color='steelblue', linewidth=2)
ax.fill_between(avg.index, avg - std, avg + std, alpha=0.2, color='steelblue')
ax.set_xlabel('Week Number')
ax.set_ylabel('Avg Retention %')
ax.set_title('Average Cohort Retention Curve')
plt.tight_layout()
plt.show()

steps = ['Homepage', 'Product', 'Cart', 'Checkout', 'Purchase']
desktop = [5000, 3800, 2300, 1500, 700]
mobile = [5000, 3400, 1800, 900, 280]

x = range(len(steps))
fig, ax = plt.subplots(figsize=(10, 5))
ax.bar([i - 0.2 for i in x], desktop, width=0.4, label='Desktop', color='steelblue')
ax.bar([i + 0.2 for i in x], mobile, width=0.4, label='Mobile', color='darkorange')
ax.set_xticks(list(x))
ax.set_xticklabels(steps)
ax.legend()
ax.set_title('Funnel by Device Type')
plt.tight_layout()
plt.show()

import numpy as np

event_counts = pd.Series([3, 5, 2, 8, 1, 12, 3, 4, 6, 20, 2, 3, 1, 7, 5])

fig, ax = plt.subplots(figsize=(8, 4))
ax.hist(event_counts, bins=15, color='steelblue', edgecolor='white')
ax.set_xlabel('Events per Session')
ax.set_ylabel('Number of Sessions')
ax.set_title('Session Event Frequency Distribution')
plt.tight_layout()
plt.show()

from collections import Counter

events_per_session = [['homepage', 'product_page', 'add_to_cart'],
                       ['homepage', 'search', 'product_page'],
                       ['homepage', 'product_page', 'purchase']]

bigrams = Counter()
for session in events_per_session:
    for pair in zip(session, session[1:]):
        bigrams[pair] += 1

print(bigrams.most_common(5))

durations = pd.Series([1.2, 3.5, 0.5, 12.3, 2.1, 8.7, 1.8, 0.3, 45.1, 4.6])

fig, ax = plt.subplots(figsize=(8, 4))
ax.hist(durations, bins=20, color='steelblue', edgecolor='white')
ax.axvline(durations.median(), color='red', linestyle='--', label=f'Median: {durations.median():.1f} min')
ax.set_xlabel('Session Duration (min)')
ax.set_ylabel('Sessions')
ax.legend()
plt.tight_layout()
plt.show()

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Panel 1: funnel
axes[0, 0].barh(FUNNEL_STEPS[::-1], users[::-1], color='steelblue')
axes[0, 0].set_title('Funnel')

# Panel 2: retention heatmap
sns.heatmap(retention_pct, ax=axes[0, 1], cmap='RdYlGn', annot=True, fmt='.0f', vmin=0, vmax=100)
axes[0, 1].set_title('Cohort Retention')

fig.suptitle('User Journey Dashboard', fontsize=16)
plt.tight_layout()
fig.savefig('user_journey_dashboard.png', dpi=150, bbox_inches='tight')
print('Dashboard saved')