How Can Pandas' `groupby` Function Efficiently Create Lists of Values from Grouped Rows?

Efficiently Grouping Rows into Lists in Pandas via Groupby

For data manipulation tasks, grouping rows into lists based on specific criteria is a common requirement. In Pandas, the groupby function provides a powerful tool for this purpose.

Suppose you have a DataFrame with two columns, 'a' and 'b':

a b
A 1
A 2
B 5
B 5
B 4
C 6

The goal is to group rows based on the 'a' column and create lists of the 'b' column for each group.

To achieve this, you can leverage the groupby function:

df.groupby('a')['b'].apply(list)

The groupby function groups the DataFrame by the 'a' column. The apply function then iterates over each group and converts the 'b' column to a list using list.

The resulting output:

a
A       [1, 2]
B    [5, 5, 4]
C          [6]
Name: b, dtype: object

This technique enables you to efficiently group rows based on a specific column and obtain lists of values for other columns within each group.

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