---
title: "How to split a list inside a Dataframe cell into rows in Pandas"
description: "Step by step instructions to "explode" a list into DataFrame rows."
author: "Bartosz Mikulski"
author_bio: "Principal AI Engineer & MLOps Architect. I bridge the gap between \"it works in a notebook\" and \"it works for 200 million users.\""
author_url: https://mikulskibartosz.name
author_linkedin: https://www.linkedin.com/in/mikulskibartosz/
author_github: https://github.com/mikulskibartosz
canonical_url: https://mikulskibartosz.name/how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas
---

I started the “What’s cooking?” Kaggle challenge and wanted to do some data analysis. The given data set consists of three columns. Unfortunately, the last one is a list of ingredients.

![An array of ingredients](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_of_ingredients.png)

I wanted to calculate how often an ingredient is used in every cuisine and how many cuisines use the ingredient. I had to split the list in the last column and use its values as rows. Additionally, I had to add the correct cuisine to every row.

Let’s look at an example. If my dataset looks like this:

```
cuisine_1,id_1,[ingredient_1, ingredient_2, ingredient_3]
cuisine_2,id_2,[ingredient_4, ingredient_5]
```

**Note that my third column is a list of strings!** If you have a column with a string that contains multiple values separated by a comma, you have to split those strings into lists of strings first!

I want that output:

```
cuisine_1,id_1,ingredient_1
cuisine_1,id_1,ingredient_2
cuisine_1,id_1,ingredient_3
cuisine_2,id_2,ingredient_4
cuisine_2,id_2,ingredient_5
```

In this article, I am going to show you how to do it in two ways. First, I will use the `for` loops. Later, I will use only built-in Pandas functions.

## The for loop way

My first idea was to iterate over the rows and put them into the structure I want. I wrote some code that was doing the job and worked correctly but did not look like Pandas code. Look at this, I dissected the data frame and rebuilt it:

```
ingredients = []
cuisines = []
ids = []
for _, row in data.iterrows():
    cuisine = row.cuisine
    identifier = row.id
    for ingredient in row.ingredients:
        cuisines.append(cuisine)
        ingredients.append(ingredient)
        ids.append(identifier)
ingredient_to_cuisine = pd.DataFrame({
    "id": ids,
    "ingredient": ingredients,
    "cuisine": cuisines
})
```

There must be a better way to do it.

## The better way

Let’s do it step by step. Before each step, I will explain what function I am going to use and why. If you are interested in the full code with no explanation, scroll to the last code snippet.

Firstly, we have to split the `ingredients` column (which contains a list of values) into new columns. It is easy to do, and the output preserves the index. The index is important. We are going to need it.

```
data.ingredients.apply(pd.Series)
```

![For testing, I limited the data set to three rows.](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_exploded.png)

Now we can merge the new columns with the rest of the data set. There is a lot of empty values, but that is fine. We will get rid of them later:

```
data.ingredients.apply(pd.Series) \
    .merge(data, left_index = True, right_index = True)
```

![Merged array](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_merge.png)

First of all, I don’t need the old `ingredients` column anymore. So, let’s drop it:

```
data.ingredients.apply(pd.Series) \
    .merge(data, right_index = True, left_index = True) \
    .drop(["ingredients"], axis = 1)
```

![Dropped ingredients column](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_drop.png)

Now we can transform the numeric columns into separate rows using the `melt` function. Note that, I use the `cuisine` and the `id` as the identifier variables:

```
data.ingredients.apply(pd.Series) \
    .merge(data, right_index = True, left_index = True) \
    .drop(["ingredients"], axis = 1) \
    .melt(id_vars = ['cuisine', 'id'], value_name = "ingredient")
```

![Melted array](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_melt.png)

It looks like the `variable` column contains the ids of the numeric columns. It is useless therefore we can remove that too:

```
data.ingredients.apply(pd.Series) \
    .merge(data, right_index = True, left_index = True) \
    .drop(["ingredients"], axis = 1) \
    .melt(id_vars = ['cuisine', 'id'], value_name = "ingredient") \
    .drop("variable", axis = 1)
```

![Dropped variable column](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_drop_variable.png)

Now, it is time to remove the empty values:

```
data.ingredients.apply(pd.Series) \
    .merge(data, right_index = True, left_index = True) \
    .drop(["ingredients"], axis = 1) \
    .melt(id_vars = ['cuisine', 'id'], value_name = "ingredient") \
    .drop("variable", axis = 1) \
    .dropna()
```

![Dropped empty values](/images/2018-08-27-how-to-split-a-list-inside-a-dataframe-cell-into-rows-in-pandas/array_dropna.png)

Done! Now we have a data set that has the ingredients in separate rows.