How to Efficiently Unnest Multiple List Columns in a Pandas DataFrame?

How to Unnest (Explode) Multiple List Columns in a pandas DataFrame Efficiently

Problem: Exploding Nested List Columns in Large Datasets

When dealing with pandas DataFrames, it is sometimes necessary to "unnest" or "explode" columns that contain lists into multiple rows. However, this can be a computationally expensive operation, especially for large datasets.

Solution: Using pandas >= 1.3

For pandas versions 1.3 and above, there is a built-in function called DataFrame.explode that allows you to unnest multiple columns simultaneously. This function requires that all list columns have the same length. To use it:

df.explode(['B', 'C', 'D', 'E']).reset_index(drop=True)

Solution for pandas

For older versions of pandas, a slightly more complex approach is required:

1. Set the index of the DataFrame to be the columns that should not be exploded.
2. Apply Series.explode to each column to be exploded.
3. Reset the index to obtain the unnested DataFrame.

df.set_index(['A']).apply(pd.Series.explode).reset_index()

Efficiency Considerations

Both methods provide efficient solutions, with set_index and explode being slightly faster than DataFrame.explode. The following table shows the performance comparison:

Method	Time (seconds)
DataFrame.explode	0.00259
Set index and explode	0.00127
Stacking approach	0.120

Note on Duplicate Question

While this question was initially marked as a duplicate, it specifically emphasizes the need for an efficient method that can handle large datasets. The answers to the duplicate question failed to adequately address this requirement.

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