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'.