A Quick Tutorial to Encode List Variables

Photo by Mika Baumeister on Unsplash

Practical data manipulation activities include categorical data that needs to be transformed in a way that a Machine Learning algorithm can understand. There are many ways of encoding this kind of data, some of which are Label Encoding or One-Hot Encoding. However, there are cases that categorical data encoding is not so straightforward, and additional data handling is necessary. One of those cases is when we have categorical data coming in a single column as a list , with each row belonging to several categories.

In this small article, I present a step-by-step tutorial on how to encode categorical data that comes as a list in a DataFrame column.

Step 0: Generate some data

We are going to create a DataFrame from a list of dictionaries. As an example, let’s use some data holding the name of a person and the cities they have visited.

import pandas as pddata = [
{'cities': ['Athens', 'London', 'Berlin'], 'person': 'John'},
{'cities': ['Athens', 'London'], 'person': 'Nick'},
{'cities': ['Berlin', 'London'], 'person': 'Helen'}
df = pd.DataFrame(data)

The resulted DataFrame we constructed has a list of elements on each of the rows for the cities column.

The original DataFrame

We will go through a number of transformations that will give us a much easier-to-handle view of the same data, that will look like this:

The desired DataFrame

Step 1: Transform the column to a list

Next step is to transform the cities column to a list and put this into a DataFrame, which we’ll call cities_df .

cities_df = pd.DataFrame(df['cities'].tolist())
cities_df: DataFrame resulted from the cities column

Note or Before applying Step 1. Sometimes, if we load the data from a file, we can’t be sure about the type of such a column. Depending on where the data comes from (e.g. derived from data crawling), the cities column might be type of str and therefore be loaded as type str; in this case, an additional preprocessing step is needed, before trying to apply the tolist() function to the column, to ensure that the input is evaluated correctly.
Using the literal_eval() function of the ast module, we can transform each element of the cities column. After that, we are ready to use the tolist() function on our data.

import ast
df['cities'] = df['cities'].apply(lambda x: ast.literal_eval(x))

Step 2: Transform columns to indexes

Then, we will need to transform our new DataFrame using stack() to stack our columns into indexes. This is a very useful function when it comes to rearranging our data and looking at it from a different perspective.

cities_obj = cities_df.stack()

and the result:

The result after applying stack()

Step 3: Convert to dummy variables

At this stage, we need to convert our categorical column into dummy variables. For this, we will use the pandas get_dummies() function, which is applied to each individual row in our object.

cities_df = pd.get_dummies(cities_obj)
The result after applying get_dummies()

Step 4: Sum on the index level

We have started to approach the desired result. The goal is to sum the rows on the index level (level=0), in order to aggregate the information in one row for each case.

cities_df = cities_df.sum(level=0)
The result applying sum()

Step 5: Re-join result with the original data

The resulting DataFrame is probably more meaningful if we re-join it with the original data that hold the rest of the information. We use concat() for this:

cities_df = pd.concat([df, cities_df], axis=1)
The result after using concat()


This article summarised the process of transforming this particular type of data. If you found this useful, apply it or like it :) otherwise, please leave feedback for alternative approaches!




Data enthusiast, Machine Learning fan. Doing data work at the BBC

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Christina Boididou

Christina Boididou

Data enthusiast, Machine Learning fan. Doing data work at the BBC

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