How to Explode Lists Within Pandas DataFrames?

Exploding Lists in Pandas

Problem Statement

In pandas, you may encounter dataframes with cells containing lists of multiple values. Instead of storing multiple values in a single cell, it can be beneficial to expand the dataframe so that each item in the list occupies its own row.

Solution for Pandas >= 0.25

Pandas version 0.25 and above introduce the .explode() method for both Series and DataFrame. This method effectively separates list elements into distinct rows.

To explode a column, simply use the following syntax:

df.explode('column_name')

For example, let's consider the following dataframe:

import pandas as pd
import numpy as np

df = pd.DataFrame(
    {'trial_num': [1, 2, 3, 1, 2, 3],
     'subject': [1, 1, 1, 2, 2, 2],
     'samples': [list(np.random.randn(3).round(2)) for i in range(6)]
    }
)

To explode the 'samples' column, we would use:

df_exploded = df.explode('samples')

This would produce the desired output:

   subject  trial_num  samples
0        1          1    0.57
1        1          1   -0.83
2        1          1    1.44
3        1          2   -0.01
4        1          2    1.13
5        1          2    0.36
6        1          3    1.18
# etc.

Note for Mixed Columns

The .explode() method can handle mixed columns of lists and scalars, as well as empty lists and NaNs. However, it's important to note that it can only explode a single column at a time.

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