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Working with Categorical Data in Python

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

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4 Hours15 Videos52 Exercises5,971 Learners4200 XPData Scientist with Python TrackData Scientist Professional with Python Track

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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.
  1. 1

    Introduction to Categorical Data

    Free

    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.

    Play Chapter Now
    Course introduction
    50 xp
    Categorical vs. numerical
    100 xp
    Exploring a target variable
    100 xp
    Ordinal categorical variables
    100 xp
    Categorical data in pandas
    50 xp
    Setting dtypes and saving memory
    100 xp
    Creating a categorical pandas Series
    100 xp
    Setting dtype when reading data
    100 xp
    Grouping data by category in pandas
    50 xp
    Create lots of groups
    50 xp
    Setting up a .groupby() statement
    100 xp
    Using pandas functions effectively
    100 xp
  2. 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.

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    Setting category variables
    50 xp
    Setting categories
    100 xp
    Adding categories
    100 xp
    Removing categories
    100 xp
    Updating categories
    50 xp
    Collapsing categories knowledge check
    50 xp
    Renaming categories
    100 xp
    Collapsing categories
    100 xp
    Reordering categories
    50 xp
    Reordering categories in a Series
    100 xp
    Using .groupby() after reordering
    100 xp
    Cleaning and accessing data
    50 xp
    Cleaning variables
    100 xp
    Accessing and filtering data
    100 xp
  3. 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.

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    Introduction to categorical plots using Seaborn
    50 xp
    Boxplot understanding
    50 xp
    Creating a box plot
    100 xp
    Seaborn bar plots
    50 xp
    Creating a bar plot
    100 xp
    Ordering categories
    100 xp
    Bar plot using hue
    100 xp
    Point and count plots
    50 xp
    Creating a point plot
    100 xp
    Creating a count plot
    100 xp
    Review catplot() types
    100 xp
    Additional catplot() options
    50 xp
    One visualization per group
    100 xp
    Updating categorical plots
    100 xp
  4. 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.

    Play Chapter Now
    Categorical pitfalls
    50 xp
    Memory usage knowledge check
    50 xp
    Overcoming pitfalls: string issues
    100 xp
    Overcoming pitfalls: using NumPy arrays
    100 xp
    Label encoding
    50 xp
    Create a label encoding and map
    100 xp
    Using saved mappings
    100 xp
    Creating a Boolean encoding
    100 xp
    One-hot encoding
    50 xp
    One-hot knowledge check
    50 xp
    One-hot encoding specific columns
    100 xp
    Wrap-up video
    50 xp

In the following tracks

Data Scientist with PythonData Scientist Professional with Python

Datasets

Adult Census IncomeAdoptable DogsTripadvisor ReviewsUsed Cars

Collaborators

Amy Peterson
Justin Saddlemyer

Prerequisites

Data Manipulation with pandas
Kasey Jones Headshot

Kasey Jones

Research Data Scientist

Kasey Jones is a research data scientist at RTI International. His work focuses primarily on agent-based model simulations and natural language processing analysis. He also enjoys creating unique visualizations using D3, and building R-Shiny and python Dash dashboards. Outside of RTI he spends his time working through leet code problems, playing chess, and traveling all over the world.
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