How to Explode List Columns in Pandas to Create Separate Rows?

Exploding List Columns in Pandas to Create Separate Rows

Exploding a list contained within a Pandas dataframe column allows for the creation of separate rows for each list value. This operation is particularly useful when dealing with data containing nested structures.

To achieve this explosion, Pandas 0.25 introduced the convenient explode() method. Consider the following example:

df = pd.DataFrame({
    'name': ['A.J. Price'] * 3,
    'opponent': ['76ers', 'blazers', 'bobcats'],
    'nearest_neighbors': [['Zach LaVine', 'Jeremy Lin', 'Nate Robinson', 'Isaia']] * 3
}).set_index(['name', 'opponent'])

This data contains a list of nearest neighbors for each player in the nearest_neighbors column. To create separate rows for each neighbor:

df_exploded = df.explode('nearest_neighbors')

Output:

                    nearest_neighbors
name       opponent                  
A.J. Price 76ers          Zach LaVine
           76ers           Jeremy Lin
           76ers        Nate Robinson
           76ers                Isaia
           blazers        Zach LaVine
           blazers         Jeremy Lin
           blazers      Nate Robinson
           blazers              Isaia
           bobcats        Zach LaVine
           bobcats         Jeremy Lin
           bobcats      Nate Robinson
           bobcats              Isaia

This technique simplifies the process of unfolding list columns, providing a clean and efficient way to work with nested data in Pandas.

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