How Can I Transform a Pandas Column of Lists into Multiple Rows?

Pandas Column of Lists: Creating a Row for Each List Element

In Pandas dataframes, it may be necessary to convert a column containing lists into multiple rows, where each element of the list occupies its own row. To achieve this, there are two viable options.

Pandas>=0.25's Built-In explode Method

For Pandas versions 0.25 and later, the .explode() method has been introduced specifically for this purpose. It efficiently converts lists into individual rows.

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)]
                   })

df.explode('samples').reset_index(drop=True)  # Resetting the index for clarity

This method handles mixed columns containing lists and scalars, as well as empty lists and NaNs. However, it is important to note that explode operates on a single column at a time.

Custom Function for Pandas

For earlier versions of Pandas, a custom function can be utilized:

def explode_list_column(df, column):
    # Create an empty list to store the expanded rows
    exploded_rows = []

    # Iterate through each cell in the specified column
    for row in df[column]:
        # Iterate through each element in the list
        for element in row:
            # Add a new row to the list, copying data from the current row and adding the new element
            exploded_rows.append(list(row) + [element])

    # Convert the expanded rows into a DataFrame
    return pd.DataFrame(exploded_rows, columns=df.columns + ['list_element'])

This function takes a DataFrame and the name of the column containing lists as parameters, and it returns a new DataFrame with a column for each list element.

# Applying the exploding function
exploded_df = explode_list_column(df, 'samples')

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