Seaborn Pairplot uses to get the relation between each and every variable present in Pandas DataFrame. It works like a seaborn scatter plot but it plot only two variables plot and sns paiplot plot the pairwise plot of multiple features/variable in a grid format.
How to plot Seaborn Pairplot?
To plot seaborn pairplot we use sns.pairplot() function.
Syntax: sns.pairplot(
data,
hue=None,
hue_order=None,
palette=None,
vars=None,
x_vars=None,
y_vars=None,
kind=’scatter’,
diag_kind=’auto’,
markers=None,
height=2.5,
aspect=1,
dropna=True,
plot_kws=None,
diag_kws=None,
grid_kws=None,
size=None,
)
For the best understanding, I suggest you follow the seaborn scatter plot and matplotlib scatter plot tutorial.
Note: Practical perform on Jupyter NoteBook and at the end of this seaborn pairplot tutorial, you will get ‘.ipynb‘ file for download.
Import Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
Load Dataset
from sklearn.datasets import load_breast_cancer
cancer_dataset = load_breast_cancer()
# create datafrmae
cancer_df = pd.DataFrame(np.c_[cancer_dataset['data'],cancer_dataset['target']],
columns = np.append(cancer_dataset['feature_names'], ['target']))
cancer_df.head(6)
Plotting Seaborn Pairplot
data: data parameter is the required parameter to pass data to plot pairplot
#plot seaborn pairplot
sns.pairplot(cancer_df)
Output>>>
vars: It hep to plot pairplot accounting to required features/variable. Pass list of variable names, optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'])
Output>>>
hue: Map the third feature to get more insights. Pass string (variable name), optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target')
Output>>>
hue_order: To change the order of hue. Pass list of strings
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', hue_order = [1.0, 0.0])
Output>>>
palette: To change the color of the seaborn pairplot, it works like cmap. Pass dict or seaborn color palette
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', palette='Dark2')
Output>>>
x_vars, y_vars: If you want required features on the x-axis and the y-axis the use it. Pass lists of variable names, optional
sns.pairplot(cancer_df, hue ='target', x_vars = ['mean radius', 'mean texture'], y_vars =['mean radius'])
Output>>>
kind: To find the linearity. Pass {‘scatter’, ‘reg’}, optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', kind = 'reg')
Output>>>
diag_kind: To change the diagonal distribution. Pass {‘auto’, ‘hist’, ‘kde’}, optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', diag_kind = 'hist')
Output>>>
markers: To change the markers of scatter dots. Pass single matplotlib marker code or list, optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', markers = ['*', "<"])
Output>>>
height: To change the height of the seaborn scatterplot. Pass scalar, optional
sns.pairplot(cancer_df, vars = ['mean radius', 'mean texture', 'mean perimeter', 'mean area',
'mean smoothness'], hue ='target', height = 20)
Output>>>
Conclusion
In the seaborn pairplot tutorial, we learn how to create a seaborn pairplot with a real-time example using sns.pairplot() function. Along with that used different functions, and parameters. We suggest you make your hand dirty with each and every parameter of the above function because This is the best coding practice. Still, you didn’t complete matplotlib tutorial then I recommend to you, catch it.
Download the above seaborn pairplot source code in Jupyter NoteBook file formate.
Official Document click here