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How to create separate rows for each list item where the list is itself an item of a pandas DataFrame column?

Given a Pandas DataFrame where a column is having a list of items, we need to create separate row for each item of columns. By Pranit Sharma Last updated : September 22, 2023

To create separate rows for each list item where the list is itself an item of a pandas DataFrame column, we will use pandas.DataFrame.explode() method. This is a special method which is used to convert each item of a column into a separate row, if the elements of a column consists of a list, then for each element of that list, pandas.DataFrame.explode() will create separate row.

Syntax:

DataFrame.explode(
    column, 
    ignore_index=False
    )

import pandas as pd

Let us understand with the help of an example,

Python code to create and print dataframe

# Importing pandas package
import pandas as pd

# Creating a Dictionary
d = {
    'List':[['fish','bird','cat','dog'],[1,2,3]],
    'Col_2':[4,5]
}

# Creating a DataFrame
df = pd.DataFrame(d)

# Display DataFrame
print("Created DataFrame:\n",df,"\n")

Output

The output of the above program is:

Now, use explode() method.

Python code to create separate rows for each list item where the list is itself an item of a DataFrame column

# Exploding the lists of column List
result = df.explode('List')

# Display result
print("Result:\n",result)

Output

The output of the above program is:

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