A **pairs plot** is a matrix of scatterplots that lets you understand the pairwise relationship between different variables in a dataset.

The easiest way to create a pairs plot in Python is to use the seaborn.pairplot(df) function.

The following examples show how to use this function in practice.

**Example 1: Pairs Plot for All Variables**

The following code shows how to create a pairs plot for every numeric variable in the seaborn dataset called **iris**:

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #define dataset iris = sns.load_dataset("iris") #create pairs plot for all numeric variables sns.pairplot(iris)

The way to interpret the matrix is as follows:

- The distribution of each variable is shown as a histogram along the diagonal boxes.
- All other boxes display a scatterplot of the relationship between each pairwise combination of variables. For example, the box in the bottom left corner of the matrix displays a scatterplot of values for
**petal_width**vs.**sepal_length**.

This single plot gives us an idea of the relationship between each pair of variables in our dataset.

**Example 2: Pairs Plot for Specific Variables**

We can also specify only certain variables to include in the pairs plot:

sns.pairplot(iris[['sepal_length', 'sepal_width']])

**Example 3: Pairs Plot with Color by Category**

We can also create a pairs plot that colors each point in each plot based on some categorical variable using the **hue** argument:

sns.pairplot(iris, hue='species')

By using the **hue** argument, we can gain an even better understanding of the data.

**Additional Resources**

How to Make Barplots with Seaborn

How to Make Heatmaps with Seaborn

How to Add a Title to Seaborn Plots