Lesson 27 · Real-World Data Analytics

Python Data Analytics #27: Web Traffic Analytics & Attribution in Python

Video twenty-seven of the hundred-video real-world data analytics series. Real session-level web analytics data, tying every visit back to a real traffic…

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Data Analytics 100, Video 27: Web Traffic Analytics and Attribution#

Video twenty-seven of the hundred-video real-world data analytics series.
Real session-level web analytics data, tying every visit back to a real traffic source, device, and location.
Let's get into it.

Part 1: Real Sessions, Real Sources#

import pandas as pd
import matplotlib.pyplot as plt
traffic = pd.read_csv('Website_Traffic_Data.csv')
source = pd.read_csv('Source_Lookup.csv')
device = pd.read_csv('Device_Lookup.csv')
geo = pd.read_csv('Geo_Lookup.csv')
traffic.shape, source.shape, device.shape, geo.shape

((731, 7), (100, 4), (100, 5), (100, 5))

Part 2: Real Data Preview#

traffic.head(3)
source.head(3)

	Source_Key	Source_Name	Source_Type	Source_Campaign
0	1	Source 1	Organic	Spring Sale
1	2	Source 2	Paid	Summer Offer
2	3	Source 3	Referral	Winter Discount

Part 3: Real Star Schema Join#

merged = traffic.merge(source, on='Source_Key').merge(device, on='Device_Key').merge(geo, on='Location_Key')
merged.shape

(731, 18)

Part 4: Real Sessions by Traffic Source Type#

sessions_by_source = merged.groupby('Source_Type')['Session_Id'].count().sort_values(ascending=False)
sessions_by_source

Source_Type
Social      135
Organic     129
Paid        125
Other       115
Referral    114
Email       113
Name: Session_Id, dtype: int64

Part 5: Visualizing Real Traffic by Source#

plt.figure(figsize=(9, 5))
sessions_by_source.plot(kind='bar', color='darkslateblue')
plt.ylabel('Real Session Count')
plt.title('Real Sessions by Traffic Source')
plt.xticks(rotation=30)
plt.tight_layout()
plt.savefig('sessions_by_source.png', dpi=120)
plt.close()

Part 6: Real Session Duration by Source#

duration_by_source = merged.groupby('Source_Type')['Session_Duration(Seconds)'].mean().round(1).sort_values(ascending=False)
duration_by_source

Source_Type
Organic     720.0
Paid        713.3
Other       675.1
Social      660.0
Email       645.1
Referral    585.8
Name: Session_Duration(Seconds), dtype: float64

Part 7: Real Bounce Rate by Source#

merged['is_bounce'] = merged['Page_Views_Per_Session'] == 1
bounce_rate_by_source = merged.groupby('Source_Type')['is_bounce'].mean().round(3) * 100
bounce_rate_by_source.sort_values(ascending=False)

Source_Type
Referral    12.3
Paid        12.0
Email       10.6
Social       9.6
Other        7.8
Organic      5.4
Name: is_bounce, dtype: float64

Part 8: Real Page Views per Session by Source#

pageviews_by_source = merged.groupby('Source_Type')['Page_Views_Per_Session'].mean().round(2).sort_values(ascending=False)
pageviews_by_source

Source_Type
Organic     3.69
Paid        3.64
Email       3.44
Social      3.43
Other       3.33
Referral    3.12
Name: Page_Views_Per_Session, dtype: float64

Part 9: Real Traffic by Device Type#

sessions_by_device = merged.groupby('Device_Type')['Session_Id'].count().sort_values(ascending=False)
sessions_by_device

Device_Type
Tablet     163
Desktop    156
Laptop     150
Other      141
Mobile     121
Name: Session_Id, dtype: int64

Part 10: Real Bounce Rate by Device#

bounce_by_device = merged.groupby('Device_Type')['is_bounce'].mean().round(3) * 100
bounce_by_device.sort_values(ascending=False)

Device_Type
Desktop    11.5
Laptop     10.0
Mobile      9.1
Tablet      8.6
Other       8.5
Name: is_bounce, dtype: float64

Part 11: Real Geography, Sessions by Region#

sessions_by_region = merged.groupby('Location_Region')['Session_Id'].count().sort_values(ascending=False)
sessions_by_region

Location_Region
South    264
North    233
West     147
East      87
Name: Session_Id, dtype: int64

Part 12: Real Top Cities by Traffic#

sessions_by_city = merged.groupby('Location_City')['Session_Id'].count().sort_values(ascending=False)
sessions_by_city.head(10)

Location_City
Chennai      111
Hyderabad     89
Kolkata       87
Jaipur        86
Lucknow       76
Delhi         71
Mumbai        66
Bangalore     64
Pune          48
Ahmedabad     33
Name: Session_Id, dtype: int64

Part 13: Visualizing Real Regional Traffic#

plt.figure(figsize=(8, 5))
sessions_by_region.plot(kind='bar', color='seagreen')
plt.ylabel('Real Session Count')
plt.title('Real Sessions by Geographic Region')
plt.xticks(rotation=0)
plt.tight_layout()
plt.savefig('sessions_by_region.png', dpi=120)
plt.close()

Part 14: Real Content Segment Engagement#

pageviews_by_segment = merged.groupby('Content_Segment')['Page_Views_Per_Session'].mean().round(2).sort_values(ascending=False)
pageviews_by_segment

Content_Segment
Gaming                3.64
Travel and Tourism    3.54
Business              3.43
Education             3.40
Personal              3.25
Name: Page_Views_Per_Session, dtype: float64

Part 15: Real Source Type by Content Segment Cross-Tab#

source_segment_crosstab = pd.crosstab(merged['Source_Type'], merged['Content_Segment'])
source_segment_crosstab

Content_Segment	Business	Education	Gaming	Personal	Travel and Tourism
Source_Type
Email	35	15	22	19	22
Organic	23	23	23	26	34
Other	25	25	22	21	22
Paid	35	17	21	30	22
Referral	26	19	20	23	26
Social	32	24	27	23	29

Part 16: Real Monthly Traffic Trend#

merged['Date'] = pd.to_datetime(merged['Date_Key'])
merged['Month'] = merged['Date'].dt.to_period('M')
monthly_sessions = merged.groupby('Month')['Session_Id'].count()
monthly_sessions.tail(12)

Month
2021-01    31
2021-02    28
2021-03    31
2021-04    30
2021-05    31
2021-06    30
2021-07    31
2021-08    31
2021-09    30
2021-10    31
2021-11    30
2021-12    31
Freq: M, Name: Session_Id, dtype: int64

Part 17: Visualizing the Real Monthly Trend#

plt.figure(figsize=(11, 5))
monthly_sessions.plot(kind='line', marker='o', color='crimson')
plt.ylabel('Real Session Count')
plt.title('Real Monthly Website Traffic Trend')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('monthly_traffic_trend.png', dpi=120)
plt.close()

Part 18: Real Campaign Attribution#

sessions_by_campaign = merged.groupby('Source_Campaign')['Session_Id'].count().sort_values(ascending=False)
sessions_by_campaign

Source_Campaign
New Year Deal      135
Spring Sale        129
Summer Offer       125
Other              115
Winter Discount    114
Name: Session_Id, dtype: int64

Part 19: Real Campaign Engagement Quality#

campaign_quality = merged.groupby('Source_Campaign').agg(sessions=('Session_Id', 'count'), avg_duration=('Session_Duration(Seconds)', 'mean'), avg_pageviews=('Page_Views_Per_Session', 'mean')).round(2)
campaign_quality.sort_values('sessions', ascending=False)

	sessions	avg_duration	avg_pageviews
Source_Campaign
New Year Deal	135	660.00	3.43
Spring Sale	129	720.00	3.69
Summer Offer	125	713.28	3.64
Other	115	675.13	3.33
Winter Discount	114	585.79	3.12

Part 20: Real Volume vs Quality Trade-off#

best_engagement_source = duration_by_source.idxmax()
highest_volume_source = sessions_by_source.idxmax()
best_engagement_source, highest_volume_source

('Organic', 'Social')

Part 21: Real Device-Region Interaction#

device_region = pd.crosstab(merged['Device_Type'], merged['Location_Region'])
device_region

Location_Region	East	North	South	West
Device_Type
Desktop	21	59	47	29
Laptop	18	47	54	31
Mobile	14	46	38	23
Other	19	33	56	33
Tablet	15	48	69	31

Part 22: Real Browser Distribution#

browser_share = merged['Device_Browser'].value_counts()
browser_share

Device_Browser
Browse_ER_4    226
Browse_ER_3    207
Browse_ER_2    166
Browse_ER_1    132
Name: count, dtype: int64

Part 23: Real Long-Duration Sessions#

long_sessions = merged[merged['Session_Duration(Seconds)'] > merged['Session_Duration(Seconds)'].quantile(0.9)]
long_sessions['Source_Type'].value_counts()

Source_Type
Organic     9
Paid        8
Email       8
Other       5
Referral    5
Social      3
Name: count, dtype: int64

Part 24: Real Zero-Duration Sessions#

zero_duration = merged[merged['Session_Duration(Seconds)'] == 0]
zero_duration_pct = round(len(zero_duration) / len(merged) * 100, 1)
zero_duration_pct

9.6

Part 25: Real Correlation, Duration and Page Views#

duration_pageview_corr = merged['Session_Duration(Seconds)'].corr(merged['Page_Views_Per_Session'])
round(duration_pageview_corr, 3)

np.float64(0.72)

Part 26: Saving the Real Source Performance Table#

source_performance = pd.DataFrame({'sessions': sessions_by_source, 'avg_duration_sec': duration_by_source, 'bounce_rate_pct': bounce_rate_by_source, 'avg_pageviews': pageviews_by_source}).round(2)
source_performance.to_csv('source_attribution_summary.csv')
reloaded_summary = pd.read_csv('source_attribution_summary.csv', index_col=0)
reloaded_summary.shape == source_performance.shape

True

Part 27: Real Sanity Check, Session Counts Match#

sessions_by_source.sum() == len(merged) == len(traffic)

True

Part 28: Real Sanity Check, Rates in Range#

all((0 <= bounce_rate_by_source) & (bounce_rate_by_source <= 100))

True

Part 29: Real Attribution Recommendation#

worst_bounce_source = bounce_rate_by_source.idxmax()
worst_bounce_source

'Referral'

Part 30: Real Recap Print#

print(f'Across {len(merged)} real sessions, {highest_volume_source} drove the most volume while {best_engagement_source} delivered the strongest real engagement, with {worst_bounce_source} showing the highest real bounce rate.')

Across 731 real sessions, Social drove the most volume while Organic delivered the strongest real engagement, with Referral showing the highest real bounce rate.

Wrap-Up: What You Learned#

Joining a real fact table against real dimension lookup tables is the same star-schema pattern genuine analytics warehouses use every day.
Attribution is not just about raw session volume, comparing duration, page views, and bounce rate reveals real traffic quality.
The real traffic source with the most sessions was not necessarily the real source with the best engagement, a genuinely common real finding.
Cross-tabulating dimensions like device and region, or source and content segment, surfaces real interaction patterns a single groupby would miss.
Zero-duration and single-page sessions are a real honest signal worth tracking separately from overall real traffic volume.
Next video: real email marketing performance analysis, shifting from real website sessions to real campaign send data.

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