How to Efficiently Split a Pandas Column of Lists into Multiple Columns?

Splitting a Pandas Column of Lists into Multiple Columns

In data exploration, it's often necessary to restructure DataFrame columns into a more manageable format. One such scenario involves splitting a column containing lists into multiple columns.

Consider a DataFrame with a single column named "teams," which holds lists of team names:

import pandas as pd

df = pd.DataFrame({
    "teams": [[
        "SF", 
        "NYG"
    ] for _ in range(7)]
})

To split this "teams" column into two columns, "team1" and "team2," we can leverages the DataFrame constructor with lists created by the to_list method.

Option 1: Modifying Existing DataFrame

Using the to_list method, we can transform the "teams" list into a list of lists, which can be used to create the new "team1" and "team2" columns:

df[['team1', 'team2']] = pd.DataFrame(df['teams'].tolist(), index=df.index)

This operation modifies the original DataFrame with the new columns:

       teams team1 team2
0  [SF, NYG]    SF   NYG
1  [SF, NYG]    SF   NYG
2  [SF, NYG]    SF   NYG
3  [SF, NYG]    SF   NYG
4  [SF, NYG]    SF   NYG
5  [SF, NYG]    SF   NYG
6  [SF, NYG]    SF   NYG

Option 2: Creating a New DataFrame

Alternatively, if desired, we can create a new DataFrame with the split columns:

df3 = pd.DataFrame(
    df['teams'].tolist(), 
    columns=['team1', 'team2']
)

This operation creates a separate DataFrame:

  team1 team2
0    SF   NYG
1    SF   NYG
2    SF   NYG
3    SF   NYG
4    SF   NYG
5    SF   NYG
6    SF   NYG

Please note that applying the apply(pd.Series) function to achieve this split is significantly slower and not recommended for larger datasets.

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