Web analytics data pattern in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the web analytics data from a CSV file.

Data Analysis Python

import pandas as pd
data = pd.read_csv([1])

Drag options to blanks, or click blank then click option'

A'web_data.csv'

Bweb_data.csv

C'data/web_analytics.csv'

Dweb_analytics.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the file name in quotes.

Using a variable name without defining it.

We need to provide the file name as a string, so it must be in quotes.

2fill in blank

medium

Complete the code to display the first 5 rows of the data.

Data Analysis Python

print(data.[1]())

Drag options to blanks, or click blank then click option'

Asample

Btail

Chead

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows the last rows.

Using info() which shows summary info, not rows.

The head() method shows the first 5 rows by default.

3fill in blank

hard

Fix the error in the code to calculate the average session duration.

Data Analysis Python

average_duration = data['session_duration'].[1]()

Drag options to blanks, or click blank then click option'

Asum

Bmean

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which adds all values.

Using count() which counts non-null entries.

The mean() method calculates the average value.

4fill in blank

hard

Fill both blanks to create a dictionary of page views for pages with more than 100 views.

Data Analysis Python

page_views = {page: data[data['page'] == page]['views'].[1]() for page in data['page'].unique() if data[data['page'] == page]['views'].[1]() [2] 100}

Drag options to blanks, or click blank then click option'

Asum

Bmean

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum().

Using < instead of > in the condition.

We sum views per page and filter pages with views greater than 100.

5fill in blank

hard

Fill all three blanks to create a filtered DataFrame with sessions longer than 5 minutes and sort by duration descending.

Data Analysis Python

filtered = data[data['session_duration'] [1] [2]].sort_values(by=[3], ascending=False)

Drag options to blanks, or click blank then click option'

A>

B300

C'session_duration'

D'duration'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong column name for sorting.

Filtering with less than or equal instead of greater than.

We filter sessions longer than 300 seconds (5 minutes) and sort by 'session_duration'.

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