Working with Categorical Data in Python Data Camp

Overview

Context

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 Working with Categorical Data in Python course offered by Data Camp 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.

What you will learn during this course:

• Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. 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.
• You will 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.
• 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 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.