Python – seaborn.pairplot() method
Last Updated :
11 Nov, 2022
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps resolve the two major issues while working with Matplotlib:
- Default Matplotlib parameters
- Working with data frames
As Seaborn compliments and enhances Matplotlib, the learning curve is quite gradual. If you are familiar with Matplotlib, you are already halfway done with Seaborn.
seaborn.pairplot() :
To plot multiple pairwise bivariate distributions in a dataset, you can use the .pairplot() function.
The diagonal plots are the univariate plots, and this displays the relationship for the (n, 2) combination of variables in a DataFrame as a matrix of plots.
seaborn.pairplot( data, \*\*kwargs )
Seaborn.pairplot uses many arguments as input, main of which are described below in form of table:
Arguments |
Description |
Value |
data |
Tidy (long-form) dataframe where each column is a variable, and each row is an observation. |
DataFrame |
hue |
Variable in “data“ to map plot aspects to different colors. |
string (variable name), optional |
palette |
Set of colors for mapping the “hue“ variable. In case of a dict, the keys should be values in the “hue“ variable. vars: list of variable names, optional |
dict or seaborn color palette |
{x, y}_vars |
Variables within “data“ to use separately for the rows and columns of the figure, i.e., to make a non-square plot. |
lists of variable names, optional |
dropna |
Drop missing values from the data before plotting. |
boolean, optional |
Below is the implementation of above method:
Example 1:
Python3
import seaborn
import matplotlib.pyplot as plt
df = seaborn.load_dataset( 'tips' )
seaborn.pairplot(df, hue = 'sex' )
plt.show()
|
Output :
Example 2:
Python3
import seaborn
import matplotlib.pyplot as plt
df = seaborn.load_dataset( 'tips' )
seaborn.pairplot(df, hue = 'day' )
plt.show()
|
Output :
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