Seaborn - Visuapzing Pairwise Relationship

Datasets under real-time study contain many variables. In such cases, the relation between each and every variable should be analyzed. Plotting Bivariate Distribution for (n,2) combinations will be a very complex and time taking process.

To plot multiple pairwise bivariate distributions in a dataset, you can use the pairplot() function. This shows the relationship for (n,2) combination of variable in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots.

Axes

In this section, we will learn what are Axes, their usage, parameters, and so on.

Usage

seaborn.pairplot(data,…)

Parameters

Following table psts down the parameters for Axes −

Sr.No.	Parameter & Description
1	data Dataframe
2	hue Variable in data to map plot aspects to different colors.
3	palette Set of colors for mapping the hue variable
4	kind Kind of plot for the non-identity relationships. {‘scatter’, ‘reg’}
5	diag_kind Kind of plot for the diagonal subplots. {‘hist’, ‘kde’}

Except data, all other parameters are optional. There are few other parameters which pairplot can accept. The above mentioned are often used params.

Example

import pandas as pd
import seaborn as sb
from matplotpb import pyplot as plt
df = sb.load_dataset( iris )
sb.set_style("ticks")
sb.pairplot(df,hue =  species ,diag_kind = "kde",kind = "scatter",palette = "husl")
plt.show()

Output

We can observe the variations in each plot. The plots are in matrix format where the row name represents x axis and column name represents the y axis.