filter() for group-level filtering in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter groups with mean value greater than 50.

Pandas

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

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

Amean

Bmax

Csum

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean() will filter by total, not average.

Using max() or min() will filter by extreme values, not average.

The mean() function calculates the average value for each group. We want groups where this average is greater than 50.

2fill in blank

medium

Complete the code to keep groups with more than 3 rows.

Pandas

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 <= will keep groups with 3 or fewer rows.

Using == keeps only groups with exactly 3 rows.

The len(x) gives the number of rows in each group. We want groups with more than 3 rows, so use >.

3fill in blank

hard

Fix the error in the code to filter groups where the max value is less than 100.

Pandas

filtered = df.groupby('category').filter(lambda x: x['value'].[1]() [2] 100)

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

Amin

Bmax

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using min() instead of max().

Using the wrong comparison operator like >.

We want the maximum value in each group to be less than 100. So use max() and the operator <.

4fill in blank

hard

Fill both blanks to filter groups where the sum of 'score' is at least 200.

Pandas

filtered = df.groupby('team').filter(lambda x: x['score'].[1]() [2] 200)

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 >=.

We want the total sum of 'score' in each group to be at least 200, so use sum() and the operator >=.

5fill in blank

hard

Fill all three blanks to filter groups where the average 'age' is less than 30 and group size is more than 5.

Pandas

filtered = df.groupby('department').filter(lambda x: x['age'].[1]() [2] 30 and len(x) [3] 5)

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

Amean

B<

C>

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean() for average.

Mixing up comparison operators.

We check if the average age is less than 30 using mean() and <, and if the group size is more than 5 using len(x) > 5.