filter() for group-level filtering in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter groups where the mean score is greater than 80.

Data Analysis Python

filtered = df.groupby('team').filter(lambda x: x['score'].[1]() > 80)

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

Amax

Bmin

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean()

Using max() or min() which do not calculate average

The mean() function calculates the average score for each group, which is needed to filter groups with mean score above 80.

2fill in blank

medium

Complete the code to filter groups where the number of rows is at least 3.

Data Analysis Python

filtered = df.groupby('category').filter(lambda x: len(x) [1] 3)

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

A>=

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of >=

Using == which only keeps groups with exactly 3 rows

The condition len(x) >= 3 keeps groups with 3 or more rows.

3fill in blank

hard

Fix the error in the code to filter groups where the maximum value in 'value' column is less than 50.

Data Analysis Python

filtered = df.groupby('group').filter(lambda x: x['value'].[1]() < 50)

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

Asum

Bmax

Cmean

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using min() which returns the smallest value

Using mean() or sum() which do not give the maximum

The max() function finds the highest value in the 'value' column for each group, which is needed to check if it's less than 50.

4fill in blank

hard

Fill both blanks to filter groups where the sum of 'points' is greater than 100 and the group size is at least 5.

Data Analysis Python

filtered = df.groupby('team').filter(lambda x: x['points'].[1]() [2] 100 and len(x) >= 5)

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

Asum

B>

C<

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum()

Using < instead of >

The sum() function calculates total points per group, and > checks if it's greater than 100.

5fill in blank

hard

Fill all three blanks to create a dictionary of group names and their average 'score' for groups with average score above 70.

Data Analysis Python

result = {group: data['score'].[1]() for group, data in df.groupby('[2]') if data['score'].[3]() > 70}

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

Amean

Bteam

Dcategory

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'category' instead of 'team' for grouping

Using sum() instead of mean() for averages

The mean() function calculates average scores. The groupby is done on 'team'. The condition checks if the average score is above 70.