After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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
Curriculum
Course outline
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 introduction50 XP
- Categorical vs. numerical100 XP
- Exploring a target variable100 XP
- Ordinal categorical variables100 XP
- Categorical data in pandas50 XP
- Setting dtypes and saving memory100 XP
- Creating a categorical pandas Series100 XP
- Setting dtype when reading data100 XP
- Grouping data by category in pandas50 XP
- Create lots of groups50 XP
- Setting up a .groupby() statement100 XP
- Using pandas functions effectively100 XP
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.
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.
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.
Working with Categorical Data in Python
Course
Complete
