GroupBy with custom functions in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the groupby() function do in pandas?

It splits the data into groups based on one or more columns, so you can perform operations on each group separately.

beginner

How can you apply a custom function to each group in pandas?

Use the .apply() method after groupby() to run your own function on each group.

intermediate

What is the difference between .agg() and .apply() in groupby?

.agg() is for simple aggregation functions like sum or mean, while .apply() lets you run any custom function on groups.

beginner

Write a simple custom function to calculate the range (max - min) of a group.

def range_func(x): return x.max() - x.min()

intermediate

Why use custom functions with groupby instead of built-in aggregations?

Custom functions let you do special calculations or transformations that built-in functions don’t support.

Which pandas method allows you to run your own function on each group after grouping?

A .transform()

B .group()

C .custom()

D .apply()

The .apply() method runs a custom function on each group after using groupby.

What does this custom function do?

def range_func(x):
    return x.max() - x.min()

A Calculates the difference between max and min values in the group

B Calculates the sum of the group

C Calculates the average of the group

D Counts the number of items in the group

The function returns the range (max - min) of values in the group.

Which of these is NOT a reason to use a custom function with groupby?

A To perform calculations not covered by built-in functions

B To automatically sort the groups alphabetically

C To apply multiple aggregations at once

D To transform data in a special way per group

Sorting groups alphabetically is not a reason to use custom functions; it's handled separately.

What will this code do?

df.groupby('Category')['Value'].apply(lambda x: x.sum())

A Sum values in 'Value' for each 'Category' group

B Sum all values in the 'Value' column ignoring groups

C Apply sum to the whole DataFrame

D Create a new column with sums

It sums the 'Value' column for each group defined by 'Category'.

Which pandas method is best for simple aggregations like mean or sum after groupby?

A .apply()

B .filter()

C .agg()

D .transform()

.agg() is designed for simple aggregation functions like mean or sum.

Explain how to use a custom function with pandas groupby to calculate a special metric for each group.

Think about splitting data, defining a function, and applying it.

Describe the difference between using .agg() and .apply() after grouping data in pandas.

One is for simple aggregations, the other for flexible custom functions.