agg() for multiple aggregations in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the agg() function do in pandas?

The agg() function lets you apply one or more aggregation operations like sum, mean, or max on DataFrame columns.

beginner

How do you apply multiple aggregations to a single column using agg()?

You pass a list of aggregation functions to agg(), like df['col'].agg(['sum', 'mean']), to get multiple results at once.

intermediate

How can you apply different aggregations to different columns in a DataFrame?

Pass a dictionary to agg() where keys are column names and values are lists of aggregation functions, e.g., df.agg({'col1': ['sum', 'mean'], 'col2': ['max']}).

beginner

What type of output does agg() produce when using multiple aggregations?

It returns a DataFrame where rows or columns represent the aggregation functions applied, showing results for each requested aggregation.

intermediate

Can you use custom functions with agg() for aggregation?

Yes, you can pass your own functions or lambda expressions to agg() to perform custom aggregation calculations.

What argument type does agg() accept to apply different aggregations on different columns?

A A boolean value

B A single string with function name

C A dictionary with column names as keys and list of functions as values

D A list of column names

To apply different aggregations on different columns, you pass a dictionary where keys are column names and values are lists of aggregation functions.

Which of these is a valid way to apply sum and mean aggregations on column 'A' using agg()?

A df['A'].agg(['sum', 'mean'])

B df.agg('sum', 'mean')

C df['A'].agg('sum', 'mean')

D df.agg(['A', 'sum', 'mean'])

Passing a list of functions like ['sum', 'mean'] to agg() on a column applies both aggregations.

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

A Aggregated results with max of A, min and mean of B

B Error because of multiple functions

C Only max of A

D Only min and mean of B

This applies max aggregation on column A and min and mean on column B, returning all results.

Can you use a lambda function inside agg()?

A No, only built-in functions are allowed

B Yes, lambda functions can be used for custom aggregation

C Only if the lambda returns a string

D Only if the lambda has no parameters

Custom functions including lambdas can be passed to agg() for flexible aggregation.

What type of object is returned by agg() when multiple aggregations are applied?

A A Series with original data

B A list of values

C A single scalar value

D A DataFrame with aggregation results

Multiple aggregations return a DataFrame showing each aggregation result clearly.

Explain how to use agg() to apply different aggregation functions to multiple columns in a pandas DataFrame.

Think about how to tell pandas which functions to use on which columns.

Describe the output format you get when using agg() with multiple aggregation functions on a DataFrame.

Consider how the results are organized for easy reading.