Skip to main content

Course

Working with Categorical Data in Python

IntermediateSkill Level

4.8+

Updated 07/2025

Learn how to manipulate and visualize categorical data using pandas and seaborn.

Start Course for Free

PythonData Manipulation4 hr15 videos52 Exercises4,200 XP34,337Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Loved by learners at thousands of companies

Training 2 or more people?

Try DataCamp for Business

Course Description

Being able to understand, use, and summarize non-numerical data—such as a person’s blood type or marital status—is a vital component of being a data scientist. In this course, you’ll learn how to manipulate and visualize categorical data using pandas and seaborn. Through hands-on exercises, you’ll get to grips with pandas' categorical data type, including how to create, delete, and update categorical columns. You’ll also work with a wide range of datasets including the characteristics of adoptable dogs, Las Vegas trip reviews, and census data to develop your skills at working with categorical data.

Prerequisites

Data Manipulation with pandas

1

Introduction to Categorical Data

Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. In this chapter, you’ll learn how pandas handles categorical columns using the data type category. You’ll also discover how to group data by categories to unearth great summary statistics.

Course introduction

Categorical vs. numerical

Exploring a target variable

Ordinal categorical variables

Categorical data in pandas

Setting dtypes and saving memory

Creating a categorical pandas Series

Setting dtype when reading data

Grouping data by category in pandas

Create lots of groups

Setting up a .groupby() statement

Using pandas functions effectively

2

Categorical pandas Series

Now it’s time to learn how to set, add, and remove categories from a Series. You’ll also explore how to update, rename, collapse, and reorder categories, before applying your new skills to clean and access other data within your DataFrame.

Setting category variables

Setting categories

Adding categories

Removing categories

Updating categories

Collapsing categories knowledge check

Renaming categories

Collapsing categories

Reordering categories

Reordering categories in a Series

Using .groupby() after reordering

Cleaning and accessing data

Cleaning variables

Accessing and filtering data

3

Visualizing Categorical Data

In this chapter, you’ll use the seaborn Python library to create informative visualizations using categorical data—including categorical plots (cat-plot), box plots, bar plots, point plots, and count plots. You’ll then learn how to visualize categorical columns and split data across categorical columns to visualize summary statistics of numerical columns.

Introduction to categorical plots using Seaborn

Boxplot understanding

Creating a box plot

Seaborn bar plots

Creating a bar plot

Ordering categories

Bar plot using hue

Point and count plots

Creating a point plot

Creating a count plot

Review catplot() types

Additional catplot() options

One visualization per group

Updating categorical plots

4

Pitfalls and Encoding

Lastly, you’ll learn how to overcome the common pitfalls of using categorical data. You’ll also grow your data encoding skills as you are introduced to label encoding and one-hot encoding—perfect for helping you prepare your data for use in machine learning algorithms.

Categorical pitfalls

Memory usage knowledge check

Overcoming pitfalls: string issues

Overcoming pitfalls: using NumPy arrays

Label encoding

Create a label encoding and map

Using saved mappings

Creating a Boolean encoding

One-hot encoding

One-hot knowledge check

One-hot encoding specific columns

Wrap-up video

Working with Categorical Data in Python

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.8

from 2,438 reviews

82%

16%

2%

0%

0%

Sort by

Kolade

7 hours ago

Sadam

2 days ago

good for categorical work on datasets

Phyo Aung

2 days ago

Ahmed

2 days ago

Claudia Elizabeth

2 days ago

Tanvir Rubayet

2 days ago

Kolade

"good for categorical work on datasets"

Sadam

Phyo Aung

FAQs

What Python libraries are used in this course?

You will use pandas for manipulating categorical data and seaborn for creating visualizations of categorical variables.

What datasets are used in the exercises?

You work with data on adoptable dog characteristics, Las Vegas trip reviews, and census data to practice categorical data manipulation and visualization.

What will I learn about the pandas categorical data type?

You will learn to create, update, and delete categorical columns, set and reorder categories, and use the category type to improve memory efficiency.

Is this course suitable for beginners?

This is an intermediate course. You should be comfortable with pandas and intermediate Python before starting.

Why is working with categorical data important?

Almost every dataset contains categorical information like status or type. Knowing how to properly encode and summarize it is essential for accurate analysis and modeling.

Join over 19 million learners and start Working with Categorical Data in Python today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Grow your data skills with DataCamp for Mobile

Make progress on the go with our mobile courses and daily 5-minute coding challenges.