Named aggregation in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is named aggregation in pandas?

Named aggregation lets you give custom names to the results of aggregation functions when using groupby.agg(). It helps make the output clearer and easier to understand.

beginner

How do you write a named aggregation for the mean of a column 'sales' with the name 'average_sales'?

You write it as: average_sales=('sales', 'mean') inside the agg() function.

intermediate

Why use named aggregation instead of just a list of functions in agg()?

Named aggregation lets you control the output column names, making the results easier to read and use later. Without it, pandas uses default names that can be confusing.

intermediate

Show an example of named aggregation with multiple columns and functions.

Example: df.groupby('category').agg(total_sales=('sales', 'sum'), avg_price=('price', 'mean')) groups by 'category' and calculates sum of sales and mean of price with clear column names.

advanced

Can named aggregation be used with custom functions?

Yes! You can use any function, including your own, by passing it as the second element in the tuple, like custom_stat=('column', my_func).

What does named aggregation help you do in pandas groupby?

A Sort the data before grouping

B Change data types of columns

C Filter rows after grouping

D Name the output columns of aggregation results

Named aggregation lets you assign custom names to the results of aggregation functions, making output clearer.

Which syntax correctly uses named aggregation to get the max of 'age' as 'max_age'?

A max('age') as max_age

B max_age=('age', 'max')

C ('age', max_age='max')

D age.max() as max_age

The correct syntax is max_age=('age', 'max') inside agg().

What will this code do? df.groupby('team').agg(total_points=('points', 'sum'))

A Sum points for each team and name the column 'total_points'

B Count points for each team

C Calculate average points for each team

D Filter teams with total points

It sums the 'points' column for each 'team' and names the result 'total_points'.

Can you use multiple named aggregations in one agg() call?

A Only if they are the same function

B No, only one aggregation per agg()

C Yes, by separating them with commas

D Only if they are on the same column

You can use many named aggregations separated by commas to get multiple results.

What type of object does groupby().agg(named_aggregation) return?

A A DataFrame with named columns

B A Series with unnamed index

C A list of values

D A scalar number

It returns a DataFrame where columns are named as specified in the named aggregation.

Explain what named aggregation is and why it is useful in pandas groupby operations.

Think about how you can label your summary results clearly.

Write a pandas groupby statement using named aggregation to calculate the average and maximum of a column 'score' grouped by 'class'.

Use tuples like ('score', 'mean') with custom names.