Introduction to Seaborn Categorical Plots

Plots are often used to depict the relationship between two or more variables. These variables can be purely numerical or represent a category, such as a group, class, or division. This article discusses categorical variables and how to visualize them using the Python Seaborn package.

In addition to being a statistical charting toolkit, Seaborn also includes a variety of default datasets. We will use one of the built-in datasets as an example of a default dataset.

Let’s consider the prompt dataset for our first example. The Tips dataset includes information about people who may have dined at a restaurant and whether they left a tip for the waiter, as well as their gender, smoking status, and other factors.

The Seaborn.get_dataset_names() method helps retrieve the names of all built-in datasets.

The Seaborn.get_dataset_names() method helps retrieve the names of all built-in datasets.

The Seaborn.get_dataset_names() method helps retrieve the names of all built-in datasets.

The Seaborn.get_dataset_names() method helps retrieve the names of all built-in datasets.

The Seaborn.get_dataset_names() method helps retrieve the names of all built-in datasets. title=”Seaborn Tutorial”>seaborn.get_dataset_names()

The

load_dataset() method helps load named datasets into a data structure.

Tips=seaborn.load_dataset('tips')

The above line of code helps load the dataset named “tips” into a data structure called tips.

There are different kinds of categorical plots, such as distribution plots, estimation plots, and scatter plots. Each of these categories contains a number of plots.