Visualizing Categorical Data in Python with Seaborn: Bar, Count, and Swarm Plots
Learn how to create and interpret categorical plots in Python using Seaborn and Matplotlib. This tutorial covers bar plots, count plots, and swarm plots, showing you when to use each type and how to apply them to real data. You will see step-by-step examples using a restaurant dataset and learn how to visualize categories like gender, day, and meal type.
Follow along as we import libraries, load data, and build insightful charts to compare averages, count group sizes, and display all individual values. Practice with hands-on exercises and a mini project, then try making your own custom plots with new data. These skills are useful for analyzing survey results, sales, or any grouped information.
00:00 Introduction to categorical plots
00:09 Overview of plot types
00:27 Importing libraries
00:41 Loading and exploring data
01:03 Understanding categorical variables
01:24 Dataset preview and categories
01:58 Bar plots explained
02:15 Bar plot example by day
02:47 Grouped bar plots with color
03:17 Count plots overview
03:34 Count plot example by day
04:05 Split count plots by group
04:40 Swarm plots introduction
05:01 Swarm plot example by day
05:33 Swarm plots with multiple groups
06:05 Choosing the right plot
06:29 Practice with meal categories
06:58 Mini project with bar plots
07:33 Practice with swarm plots
08:10 Extra tips for plotting
08:32 Challenge exercise with your own data
09:47 Recap and summary
10:10 Next steps and call to action
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