How to Efficiently Split a String Column in a Pandas DataFrame into Two New Columns?

How to Split Dataframe String Column into Two Columns?

TL;DR version:

For the simple case of having a text column with a delimiter and wanting to create two columns, the simplest solution is:

df[['A', 'B']] = df['AB'].str.split(' ', n=1, expand=True)

In detail:

Andy Hayden's approach effectively demonstrates the power of the str.extract() method. However, for a simple split over a known separator, the .str.split() method is sufficient. It operates on a column (Series) of strings and returns a column (Series) of lists.

The .str attribute of a column allows us to treat each element in a column as a string and apply methods efficiently. It has an indexing interface for getting each element of a string by its index, enabling us to slice and dice lists returned from .str.split().

Python tuple unpacking can be used to create two separate columns from the list using:

df['A'], df['B'] = df['AB'].str.split('-', n=1).str

Alternatively, one can utilize the expand=True parameter in .str.split() to directly generate two columns:

df[['A', 'B']] = df['AB'].str.split('-', n=1, expand=True)

The expand=True version is advantageous when dealing with splits of different lengths, as it handles such cases by inserting None values in the columns with missing "splits".

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