Web analytics data pattern in Data Analysis Python - Mini Project: Build & Apply

Web analytics data pattern

📖 Scenario: You work as a data analyst for a website. You have daily visitor counts for a week. You want to find which days had visitor counts above a certain threshold to understand busy days.

🎯 Goal: Build a small program that stores daily visitor counts, sets a threshold, finds days with visitors above that threshold, and prints those days with their counts.

📋 What You'll Learn

Create a dictionary with exact day names as keys and visitor counts as values

Create a variable for the visitor threshold

Use a dictionary comprehension to find days with visitors above the threshold

Print the resulting dictionary of busy days

💡 Why This Matters

🌍 Real World

Web analysts often need to find patterns in visitor data to understand peak usage days and plan resources accordingly.

💼 Career

This skill helps data analysts and marketers identify important trends and make data-driven decisions for website improvements.

1

Create the daily visitors dictionary

Create a dictionary called daily_visitors with these exact entries: 'Monday': 120, 'Tuesday': 85, 'Wednesday': 150, 'Thursday': 90, 'Friday': 200, 'Saturday': 75, 'Sunday': 110.

Data Analysis Python

# Create the daily_visitors dictionary with exact day names and counts
# Your code here

Hint

Use curly braces {} to create a dictionary. Put day names as keys and visitor counts as values.

2

Set the visitor threshold

Create a variable called visitor_threshold and set it to 100.

Data Analysis Python

daily_visitors = {'Monday': 120, 'Tuesday': 85, 'Wednesday': 150, 'Thursday': 90, 'Friday': 200, 'Saturday': 75, 'Sunday': 110}
# Create visitor_threshold variable and set it to 100
# Your code here

Hint

Just assign the number 100 to the variable visitor_threshold.

3

Find busy days using dictionary comprehension

Use a dictionary comprehension to create a new dictionary called busy_days that contains only the days from daily_visitors where the visitor count is greater than visitor_threshold. Use day and count as the loop variables.

Data Analysis Python

daily_visitors = {'Monday': 120, 'Tuesday': 85, 'Wednesday': 150, 'Thursday': 90, 'Friday': 200, 'Saturday': 75, 'Sunday': 110}
visitor_threshold = 100
# Create busy_days dictionary with days having counts > visitor_threshold
# Your code here

Hint

Use {day: count for day, count in daily_visitors.items() if count > visitor_threshold} to filter days.

4

Print the busy days dictionary

Write a print statement to display the busy_days dictionary.

Data Analysis Python

daily_visitors = {'Monday': 120, 'Tuesday': 85, 'Wednesday': 150, 'Thursday': 90, 'Friday': 200, 'Saturday': 75, 'Sunday': 110}
visitor_threshold = 100
busy_days = {day: count for day, count in daily_visitors.items() if count > visitor_threshold}
# Print the busy_days dictionary
# Your code here

Hint

Use print(busy_days) to show the filtered dictionary.

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