What is seaborn.pairplot?

Overview

Seaborn is a data visualization library in Python that provides various functions for different charts and plots.

Refer to Seaborn library in Python to learn more about seaborn.

A pair plot is a plot of subplots where each subplot represents a bivariate distribution of two variables in the given dataset.

Here is an example of a pair plot for the iris dataset:

Iris dataset
Iris dataset

Syntax

seaborn.pairplot(data, hue=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', dropna=False)

Parameters

  • data: This is a pandas DataFrame where each column is a variable and each row is an observation.
  • hue: This is a variable in data to map to different colors.
  • x_vars, y_vars: These are the variables in the data to be used separately for the rows and columns of the plot.
  • kind: This is the kind of plot to be made. Its values can be scatter, kde, hist, or reg.
  • diag_kind: This is the kind of plot for the diagonal subplots. Its values can be auto, hist, kde, or None.
  • dropna: This is a boolean value that indicates whether to drop the missing values before plotting.

Example

import seaborn as sns
import matplotlib.pyplot as plt
taxis = sns.load_dataset("taxis")
p_plot = sns.pairplot(taxis)
plt.savefig("output/plot.png")

Explanation

  • Lines 1–2: We import the seaborn and matplotlib libraries.
  • Line 4: The seaborn library provides a predefined set of datasets. The different datasets can be found in seaborn datasets. One such dataset is the taxis dataset. The dataset is loaded into memory using the load_dataset() function.
  • Line 6: A pair plot is created using the pairplot() for the taxis dataset.
  • Line 8: The plot is saved to the local disk as plot.png.

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