How to Efficiently Join Strings Within Pandas Groupby Results?

Pandas groupby with Delimiter Join

In Pandas, using the groupby function can be useful when working with data containing duplicate values. However, if you wish to obtain a summarized value while retaining the distinct values in a group, implementing a custom join operation may be necessary.

Consider the following example:

col  val
A    Cat
A    Tiger
B    Ball
B    Bat

When using the groupby function to sum the 'val' column for each unique value in 'col', the following output is generated:

A CatTiger
B BallBat

To introduce a delimiter (e.g., '-') into the joined values, the following code can be used:

df.groupby(['col'])['val'].sum().apply(lambda x: '-'.join(x))

However, this approach leads to an unexpected result:

A C-a-t-T-i-g-e-r
B B-a-l-l-B-a-t

The issue arises due to the lambda function receiving a Series object containing the individual values from the 'val' column instead of the concatenated string.

The following alternative approach can be used to achieve the desired delimiter-joined output:

df.groupby('col')['val'].agg('-'.join)

This provides the output:

col
A    Cat-Tiger
B     Ball-Bat
Name: val, dtype: object

To convert the index or MultiIndex to columns, you can use the reset_index function:

df1 = df.groupby('col')['val'].agg('-'.join).reset_index(name='new')

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