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.

Practice

1. What does a web analytics data pattern typically help you understand about website visitors?

easy

A. The website's server hardware specifications

B. The programming language used to build the website

C. How visitors interact with the website

D. The color scheme of the website

Solution

1. Step 1: Understand the purpose of web analytics data

   Web analytics data tracks visitor actions like page views and clicks.

2. Step 2: Identify what patterns reveal

   Patterns show how visitors interact, such as popular pages and visit duration.

3. Final Answer:

   How visitors interact with the website -> Option C

4. Quick Check:

   Visitor interaction = Understanding web analytics data [OK]

Hint: Web analytics = visitor behavior on site [OK]

Common Mistakes:

• Confusing analytics with technical website details
• Thinking analytics shows website design choices
• Assuming analytics reveals server specs

2. Which Python code snippet correctly counts page visits from a list of page names called pages?

easy

A. from collections import Counter
visit_counts = Counter(pages)

B. visit_counts = pages.count()

C. visit_counts = sum(pages)

D. visit_counts = pages.length()

Solution

1. Step 1: Identify correct method to count items in a list

   Python's collections.Counter counts occurrences of each item in a list.

2. Step 2: Check each option's validity

   from collections import Counter visit_counts = Counter(pages) uses Counter(pages) correctly. Others use invalid methods or wrong functions.

3. Final Answer:

   from collections import Counter\nvisit_counts = Counter(pages) -> Option A

4. Quick Check:

   Use Counter to count list items [OK]

Hint: Use Counter to count list items quickly [OK]

Common Mistakes:

• Using .count() without argument
• Trying to sum strings
• Using .length() which is not a Python method

3. Given this code, what is the output?

import pandas as pd

# Sample data
web_data = pd.DataFrame({
    'page': ['home', 'about', 'home', 'contact', 'home', 'about'],
    'duration': [30, 45, 25, 10, 35, 50]
})

# Calculate average duration per page
avg_duration = web_data.groupby('page')['duration'].mean().round(1)
print(avg_duration)

medium

A. page
about 47.5
contact 10.0
home 30.0
Name: duration, dtype: int64

B. page
about 45.0
contact 10.0
home 30.0
Name: duration, dtype: float64

C. page
about 47.5
contact 10.0
home 30.5
Name: duration, dtype: float64

D. page
about 47.5
contact 10.0
home 30.0
Name: duration, dtype: float64

Solution

1. Step 1: Group data by 'page' and calculate mean duration

   For 'about': (45 + 50) / 2 = 47.5; 'contact': 10; 'home': (30 + 25 + 35) / 3 = 30.0

2. Step 2: Round the means to 1 decimal place

   Values are 47.5, 10.0, and 30.0 respectively, matching page about 47.5 contact 10.0 home 30.0 Name: duration, dtype: float64.

3. Final Answer:

   page\nabout 47.5\ncontact 10.0\nhome 30.0\nName: duration, dtype: float64 -> Option D

4. Quick Check:

   Mean durations rounded = page about 47.5 contact 10.0 home 30.0 Name: duration, dtype: float64 [OK]

Hint: Calculate group means carefully and round [OK]

Common Mistakes:

• Incorrect averaging of durations
• Not rounding results
• Confusing data types in output

4. The following code is intended to find the most visited page, but it raises an error. What is the error?

import pandas as pd

web_data = pd.DataFrame({
    'page': ['home', 'about', 'home', 'contact', 'home', 'about']
})

most_visited = web_data['page'].max()
print(most_visited)

medium

A. The DataFrame is missing a 'visits' column

B. Using max() returns the lexicographically largest page, not the most visited

C. max() function is not defined for pandas Series

D. The code has a syntax error in DataFrame creation

Solution

1. Step 1: Understand what max() does on a string column

   max() returns the largest value alphabetically, not the count of visits.

2. Step 2: Identify correct method to find most visited page

   Use value_counts().idxmax() to find the page with highest visits.

3. Final Answer:

   Using max() returns the lexicographically largest page, not the most visited -> Option B

4. Quick Check:

   max() on strings = lex order, not frequency [OK]

Hint: Use value_counts() to find most visited page [OK]

Common Mistakes:

• Assuming max() finds most frequent item
• Expecting max() to count occurrences
• Confusing max() with mode()

5. You have a DataFrame visits with columns user_id, page, and duration (seconds spent). How would you find the average duration per user for only pages visited more than twice by that user?

hard

A. Group by user and page, filter groups with count > 2, then average durations per user

B. Filter rows where duration > 2, then group by user and average duration

C. Group by user, count pages, then average duration ignoring counts

D. Sum durations per user and divide by total pages visited

Solution

1. Step 1: Group data by user and page to count visits

   Use groupby(['user_id', 'page']) and filter groups with count > 2.

2. Step 2: Calculate average duration per user from filtered data

   After filtering, group by user_id and compute mean duration.

3. Final Answer:

   Group by user and page, filter groups with count > 2, then average durations per user -> Option A

4. Quick Check:

   Filter by visit count, then average durations per user [OK]

Hint: Filter groups by count > 2 before averaging durations [OK]

Common Mistakes:

• Filtering by duration instead of visit count
• Ignoring grouping by page before filtering
• Averaging durations without filtering visits