How to use the explode function In Python Pandas

The way of flattening nested Series objects and DataFrame columns by splitting their content into multiple rows is known as the explode function. In this method, we will see how we can unnest multiple list columns using the explode function.

Syntax:

df=df.explode([‘Favourite Ice-cream’, ‘Favourite Soft-Drink’]).reset_index(drop=True)

Here,

• column-1, column-2: These are the columns that you want to unnest.
• df: It is the data frame that has those nested columns.

Implementations:

In this example, we have created a dataset, which has three columns, Name, Favourite Ice-cream and Favourite Soft-Drink, out of which Favourite Ice-cream and Favourite Soft-Drink columns are nested. We have unnested those columns using the explode function.

Output:

Actual dataframe:
       Name        Favourite Ice-cream   Favourite Soft-Drink
0     Arun  [Strawberry, Choco-chips]  [Coca Cola, Lemonade]
1   Aniket   [Vanilla, Black Currant]    [Thumbs Up, Sprite]
2   Ishita  [Butterscotch, Chocolate]   [Moutain Dew, Fanta]
3   Raghav       [Mango, Choco-chips]       [Mirinda, Maaza]
4  Vinayak     [Kulfi, Kaju-Kishmish]          [7Up, Sprite]
Dataframe after unnesting:
       Name Favourite Ice-cream Favourite Soft-Drink
0     Arun          Strawberry            Coca Cola
1     Arun         Choco-chips             Lemonade
2   Aniket             Vanilla            Thumbs Up
3   Aniket       Black Currant               Sprite
4   Ishita        Butterscotch          Moutain Dew
5   Ishita           Chocolate                Fanta
6   Raghav               Mango              Mirinda
7   Raghav         Choco-chips                Maaza
8  Vinayak               Kulfi                  7Up
9  Vinayak       Kaju-Kishmish               Sprite

An open-source manipulation tool that is used for handling data is known as Pandas. Have you ever encountered a dataset that has columns with data as a list? In such cases, there is a necessity to split that column into various columns, as Pandas cannot handle such data. In this article, we will discuss the same, i.e., unnest or explode multiple list columns into a Pandas data frame.