- Seaborn Tutorial
- Seaborn - Home
- Seaborn - Introduction
- Seaborn - Environment Setup
- Importing Datasets and Libraries
- Seaborn - Figure Aesthetic
- Seaborn- Color Palette
- Seaborn - Histogram
- Seaborn - Kernel Density Estimates
- Visualizing Pairwise Relationship
- Seaborn - Plotting Categorical Data
- Distribution of Observations
- Seaborn - Statistical Estimation
- Seaborn - Plotting Wide Form Data
- Multi Panel Categorical Plots
- Seaborn - Linear Relationships
- Seaborn - Facet Grid
- Seaborn - Pair Grid
- Function Reference
- Seaborn - Function Reference
- Seaborn Useful Resources
- Seaborn - Quick Guide
- Seaborn - Useful Resources
- Seaborn - Discussion
Seaborn Categorial Plots - Introduction
Plots are mostly used to depict the relationship between two or more variables. Those variables can be entirely numerical or represent a category such as a group, class, or division. This article discusses categorical variables and how they may be visualized with Python's Seaborn package.
Seaborn contains various default datasets in addition to being a statistical charting toolkit. We'll use the one of the in-built datasets as an example of a default dataset.
Let us consider the tips dataset in the first example. The 'tips' dataset comprises information about people who have likely eaten at a restaurant and whether or not they left a tip for the servers, as well as their gender, smoking status, and other factors.
Seaborn.get_dataset_names() method helps to retrieve all the names of the in-built datasets.
seaborn.get_dataset_names()
The load_dataset() method helps to load the dataset with the name into a data structure.
Tips=seaborn.load_dataset('tips')
The above line of code helps to load the dataset with the name 'tips' into a data structure called tips.
There are different kinds of categorical plots such as distribution, estimate and scatter plots. Each of these are categories contain a few plots each.
S.No | Type | Plot Names |
---|---|---|
1 | Categorical Scatter Plots | |
2 | Categorical Distribution Plots | |
3 | Categorical Estimate Plots |
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