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Working with Categorical Data in Python

4.6+
31 reviews
Intermediate

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

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4 Hours15 Videos52 Exercises
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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.
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In the following Tracks

Certification Available

Associate Data Scientist in Python

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  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.

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    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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  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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  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.

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In the following Tracks

Certification Available

Associate Data Scientist in Python

Go To Track

Datasets

Adult Census IncomeAdoptable DogsTripadvisor ReviewsUsed Cars

Collaborators

Collaborator's avatar
Amy Peterson
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Justin Saddlemyer
Kasey Jones HeadshotKasey Jones

Research Data Scientist

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Don’t just take our word for it

*4.6
from 31 reviews
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  • Ankush B.
    about 2 months

    Very well prepared course, covering every important concept related to categorical data.

  • Andrew K.
    2 months

    See title of my review

  • Sait O.
    3 months

    Very useful course

  • Sue D.
    4 months

    Interesting course and awesome instructor!

  • Takuma K.
    7 months

    GOOD

"Very well prepared course, covering every important concept related to categorical data."

Ankush B.

"See title of my review"

Andrew K.

"Very useful course"

Sait O.

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