Web analytics data pattern in Data Analysis Python

Introduction

Web analytics data patterns help us understand how visitors behave on a website. This helps improve user experience and business decisions.

To find out which pages visitors like the most on your website.

To see what time of day most users visit your site.

To track how many visitors come back after their first visit.

To identify if visitors leave the site quickly or stay longer.

To compare visitor behavior before and after a website change.

Syntax

Data Analysis Python

import pandas as pd

# Load web analytics data
web_data = pd.read_csv('web_analytics.csv', parse_dates=['visit_time'])

# Example pattern: Count visits per page
page_visits = web_data['page'].value_counts()

# Example pattern: Average time spent per page
avg_time = web_data.groupby('page')['time_spent'].mean()

Use value_counts() to count how often each page is visited.

Use groupby() with aggregation to find average or total values per group.

Examples

This counts visits per page and shows the top pages.

Data Analysis Python

page_visits = web_data['page'].value_counts()
print(page_visits.head())

This calculates the average time visitors spend on each page.

Data Analysis Python

avg_time = web_data.groupby('page')['time_spent'].mean()
print(avg_time.head())

This counts visits by hour of the day to find peak visit times.

Data Analysis Python

visits_per_hour = web_data['visit_time'].dt.hour.value_counts().sort_index()
print(visits_per_hour)

Sample Program

This program loads sample web analytics data, then finds how many visits each page got, the average time spent on each page, and how visits are spread by hour of the day.

Data Analysis Python

import pandas as pd
from io import StringIO

# Sample web analytics data as CSV text
csv_data = '''
user_id,page,time_spent,visit_time
1,Home,30,2024-06-01 09:15:00
2,About,45,2024-06-01 10:00:00
3,Home,20,2024-06-01 09:45:00
4,Contact,15,2024-06-01 11:30:00
5,Home,25,2024-06-01 09:50:00
6,About,35,2024-06-01 10:15:00
7,Home,40,2024-06-01 09:20:00
8,Contact,10,2024-06-01 11:45:00
'''

# Read data into DataFrame
web_data = pd.read_csv(StringIO(csv_data), parse_dates=['visit_time'])

# Count visits per page
page_visits = web_data['page'].value_counts()

# Calculate average time spent per page
avg_time = web_data.groupby('page')['time_spent'].mean()

# Count visits by hour
visits_per_hour = web_data['visit_time'].dt.hour.value_counts().sort_index()

print('Visits per page:')
print(page_visits)
print('\nAverage time spent per page:')
print(avg_time)
print('\nVisits per hour:')
print(visits_per_hour)

Important Notes

Make sure your date/time columns are parsed as datetime objects for time-based analysis.

Grouping data helps find patterns like average time or counts per category.

Sorting results can make patterns easier to see.

Summary

Web analytics data patterns show how visitors interact with a website.

Use counting and grouping to find popular pages and visit times.

Analyzing time spent helps understand visitor engagement.