Multiple aggregation functions in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does applying multiple aggregation functions to a pandas DataFrame groupby object mean?

It means calculating more than one summary statistic (like mean, sum, count) at the same time for each group in the data.

beginner

How do you apply multiple aggregation functions to a single column in pandas?

Use the .agg() method with a list of functions, for example: df.groupby('col').agg({'data_col': ['mean', 'sum']})

intermediate

What is the difference between using a list and a dictionary inside the agg() function?

A list applies multiple functions to the same column(s). A dictionary lets you apply different functions to different columns.

beginner

Can you name three common aggregation functions used in pandas?

Yes, common functions include mean (average), sum (total), and count (number of items).

intermediate

What type of output do you get when you use multiple aggregation functions on a groupby object?

You get a DataFrame with hierarchical columns showing each aggregation function result for each group.

Which pandas method allows you to apply multiple aggregation functions to grouped data?

A filter()

B apply()

C transform()

D agg()

The agg() method is designed to apply one or more aggregation functions to grouped data.

What will df.groupby('A').agg({'B': ['mean', 'sum']}) return?

A Mean and sum of column A for each group in B

B Mean and sum of column B for each group in A

C Sum of columns A and B

D Count of rows in each group

It calculates mean and sum of column B for each group defined by column A.

If you want to apply different functions to different columns, what should you pass to agg()?

A A dictionary mapping columns to functions

B A list of functions

C A single function

D A boolean mask

A dictionary lets you specify which functions to apply to which columns.

What kind of columns does the output DataFrame have after multiple aggregations?

A Only index columns

B Flat columns with single names

C Hierarchical (multi-level) columns

D No columns, just a Series

Multiple aggregations create multi-level columns showing each function applied.

Which of these is NOT a typical aggregation function in pandas?

A plot

B sum

C count

D mean

plot is for visualization, not aggregation.

Explain how to use pandas to calculate both the average and total sales per product category.

Think about grouping by category and applying multiple functions to sales.

Describe the structure of the DataFrame output when multiple aggregation functions are applied to grouped data.

Focus on how columns are labeled after aggregation.