filter() for group-level filtering in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the filter() function do when used with groups in data analysis?

It keeps or removes entire groups based on a condition applied to the group as a whole, not individual rows.

beginner

How do you apply filter() to keep groups where the group size is greater than 3?

Use df.groupby('column').filter(lambda x: len(x) > 3) to keep groups with more than 3 rows.

beginner

True or False: filter() can be used to keep groups based on the average value of a column in that group.

True. You can write a condition like lambda x: x['col'].mean() > value inside filter().

intermediate

What type of object does filter() receive when used after groupby()?

It receives a DataFrame representing each group, allowing you to check group-level properties.

intermediate

Why is filter() useful compared to filtering rows individually after grouping?

Because it lets you keep or remove whole groups based on group-level criteria, making group analysis easier.

What does df.groupby('A').filter(lambda x: len(x) > 2) do?

A Removes groups with more than 2 rows

B Keeps groups where the number of rows is more than 2

C Keeps rows where column 'A' is greater than 2

D Filters rows where length of column 'A' is greater than 2

The filter keeps entire groups where the group size (number of rows) is greater than 2.

Which of these is a valid condition inside filter() for group-level filtering?

A lambda x: x['score'].mean() > 50

B lambda x: x['score'] > 50

C lambda x: x['score'].sum()

D lambda x: x['score'].max()

The condition must return True or False. Only option A returns a boolean condition.

What type of object is passed to the function inside filter() after groupby()?

A A DataFrame for each group

B A Series of group keys

C A list of group names

D A single row from the DataFrame

The function receives each group's DataFrame to check group-level conditions.

If you want to keep groups where the maximum value in column 'sales' is above 100, which filter condition is correct?

A lambda x: x['sales'].mean() > 100

B lambda x: x['sales'] > 100

C lambda x: x['sales'].max() > 100

D lambda x: len(x) > 100

Option C checks the max value in 'sales' per group and returns True if above 100.

What happens to groups that do not meet the filter() condition?

A They are sorted to the end

B Only some rows are removed from the group

C They are marked but kept

D They are removed entirely from the result

Groups failing the condition are completely removed from the filtered DataFrame.

Explain how filter() works with groupby() to keep or remove groups.

Think about how you decide which friend groups to keep based on a group trait.

Describe a real-life example where group-level filtering with filter() would be useful.

Imagine sorting groups of items or people based on a group property.