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.

Practice

1. What does the filter() function do when applied to a grouped pandas DataFrame?

easy

A. Sorts groups by their size

B. Filters rows inside each group individually

C. Keeps or removes entire groups based on a condition

D. Aggregates data by calculating group means

Solution

1. Step 1: Understand the purpose of filter() on groups

   The filter() function is used on grouped data to keep or remove whole groups based on a condition applied to the group as a whole.

2. Step 2: Differentiate from row-level filtering

   Unlike filtering rows inside groups, filter() returns entire groups only if the condition is True for that group.

3. Final Answer:

   Keeps or removes entire groups based on a condition -> Option C

4. Quick Check:

   Group-level filtering = Keeps/removes groups [OK]

Hint: Remember: filter() keeps or drops whole groups [OK]

Common Mistakes:

• Thinking filter() filters rows inside groups
• Confusing filter() with aggregation functions
• Assuming filter() sorts groups

2. Which of the following is the correct syntax to filter groups in a pandas DataFrame df grouped by column 'Category' to keep groups with more than 3 rows?

easy

A. df.groupby('Category').filter(lambda x: len(x) > 3)

B. df.filter(groupby='Category', condition=lambda x: len(x) > 3)

C. df.groupby('Category').apply(filter(len(x) > 3))

D. df.filter(lambda x: x['Category'] > 3)

Solution

1. Step 1: Recall correct filter() syntax on grouped data

   The correct syntax is to group by a column, then call filter() with a function that takes each group and returns True or False.

2. Step 2: Check each option

   df.groupby('Category').filter(lambda x: len(x) > 3) correctly groups by 'Category' and filters groups with length > 3. Options A, B, and D use incorrect syntax or wrong method calls.

3. Final Answer:

   df.groupby('Category').filter(lambda x: len(x) > 3) -> Option A

4. Quick Check:

   Correct syntax = groupby + filter(lambda x: condition) [OK]

Hint: Use groupby().filter(lambda x: condition) for group filtering [OK]

Common Mistakes:

• Using filter() without groupby()
• Passing wrong arguments to filter()
• Confusing apply() with filter()

3. Given the DataFrame df below:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 7, 20]}
df = pd.DataFrame(data)
filtered = df.groupby('Team').filter(lambda x: x['Points'].sum() > 20)
print(filtered)

What will be the output?

medium

A. Rows where Team is 'A' and 'C'

B. Rows where Team is 'A' and 'B'

C. Rows where Team is 'B' only

D. All rows

Solution

1. Step 1: Calculate sum of Points per Team

   Team A: 10 + 15 = 25, Team B: 10 + 5 + 7 = 22, Team C: 20

2. Step 2: Apply filter condition sum > 20

   Teams A and B have sums > 20, Team C has 20 (not > 20), so only A and B groups pass.

3. Step 3: Check printed rows

   Filtered DataFrame includes rows from Teams A and B only.

4. Final Answer:

   Rows where Team is 'A' and 'B' -> Option B

5. Quick Check:

   Sum > 20 keeps Teams A and B [OK]

Hint: Sum group values, keep groups where sum > 20 [OK]

Common Mistakes:

• Forgetting to include Team A rows
• Including Team C because sum equals 20
• Confusing sum with mean

4. Consider this code snippet:

df.groupby('Category').filter(lambda x: x['Value'] > 10)

Why will this code raise an error?

medium

A. Because the lambda returns a Series, not a single True/False value

B. Because filter() cannot be used after groupby()

C. Because 'Value' column does not exist

D. Because filter() requires a list, not a lambda

Solution

1. Step 1: Understand filter() expects a boolean scalar per group

   The function inside filter() must return a single True or False for the whole group.

2. Step 2: Analyze the lambda function

   The lambda returns a Series of booleans (x['Value'] > 10) for each row, not a single boolean.

3. Final Answer:

   Because the lambda returns a Series, not a single True/False value -> Option A

4. Quick Check:

   filter() needs single True/False per group [OK]

Hint: Lambda must return one True/False per group, not Series [OK]

Common Mistakes:

• Returning a Series instead of a scalar boolean
• Thinking filter() works like row-level filter
• Misunderstanding filter() usage after groupby

5. You have a sales DataFrame df with columns 'Store', 'Sales', and 'Month'. You want to keep only stores where the average monthly sales are above 5000. Which code correctly filters the DataFrame using filter()?

hard

A. df.filter(lambda x: x['Sales'].mean() > 5000, groupby='Store')

B. df.groupby('Store').filter(lambda x: x['Sales'] > 5000)

C. df.groupby('Store').apply(lambda x: x['Sales'].mean() > 5000)

D. df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000)

Solution

1. Step 1: Identify correct use of filter() with groupby

   We must group by 'Store' and use filter() with a lambda returning True if the group's average sales > 5000.

2. Step 2: Check each option

   df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000) correctly uses filter() with lambda x: x['Sales'].mean() > 5000. df.filter(lambda x: x['Sales'].mean() > 5000, groupby='Store') has wrong syntax, C uses apply which returns a Series, and D compares sales values directly, not the mean.

3. Final Answer:

   df.groupby('Store').filter(lambda x: x['Sales'].mean() > 5000) -> Option D

4. Quick Check:

   Use groupby + filter with mean condition [OK]

Hint: Filter groups by mean with groupby().filter(lambda x: x['col'].mean() > val) [OK]

Common Mistakes:

• Using filter() without groupby()
• Comparing individual sales instead of group mean
• Using apply() instead of filter() for filtering groups