Different categories of plot in Seaborn
Plots are basically used for visualizing the relationship between variables. Those variables can be either completely numerical or a category like a group, class, or division. Seaborn divides the plot into the below categories –
- Relational plots: This plot is used to understand the relation between two variables.
- Categorical plots: This plot deals with categorical variables and how they can be visualized.
- Distribution plots: This plot is used for examining univariate and bivariate distributions
- Regression plots: The regression plots in Seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses.
- Matrix plots: A matrix plot is an array of scatterplots.
- Multi-plot grids: It is a useful approach to draw multiple instances of the same plot on different subsets of the dataset.
Introduction to Seaborn – Python
Prerequisite – Matplotlib Library
Visualization is an important part of storytelling, we can gain a lot of information from data by simply just plotting the features of data. Python provides a numerous number of libraries for data visualization, we have already seen the Matplotlib library in this article we will know about Seaborn Library.