- 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 Multi plot grids - Introduction
We'll look at multi-dimensional plot data in this post. Drawing the same plot numerous times on various dataset subsets is a valuable strategy. It enables a viewer to swiftly extract a significant amount of data from a complicated dataset. We will plot numerous graphs in Seaborn in two different ways. With the first using the Facetgrid() method and the second implicitly using Matplotlib.
There are different multi-plot grids available in seaborn and they are listed below.
| S.No | Name and Description |
|---|---|
| 1 | FacetGrid() The FacetGrid class is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset. |
| 2 | Pairplot() Is used to plotpairwise relationships in a dataset. |
| 3 | PairGrid() Is used to subplot grid for plotting pairwise relationships in a dataset. |
| 4 | Jointplot() Is used to draw a plot of two variables with bivariate and univariate graphs. |
| 5 | JointGrid() Is used as a Grid for drawing a bivariate plot with marginal univariate plots |
Before moving on to understanding the working of these plots, we will understand how to load in-built datasets from the seaborn library since we will be using these datasets to learn about these functions.
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.
The Seaborn.get_dataset_names() method helps to retrieve all the names of the in-built datasets.
seaborn.get_dataset_names()
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.
Now that we know where to load datasets from, we can move onto understanding the working of multi-plot grids in seaborn.