Learn Pair Plot | Plotting with Seaborn

Definition

A pair plot visualizes pairwise relationships between all numeric variables in a dataset. Unlike a joint plot, it is not limited to two variables. It creates an N×N grid of subplots, where N is the number of numeric columns in the DataFrame.

Pair Plot Description

Each column in the grid shares the same x-axis variable, and each row shares the same y-axis. The diagonal displays histograms of individual variables, while off-diagonal cells show scatter plots.

Creating a Pair Plot

You can create one using seaborn.pairplot(). Its only required argument is data, which must be a DataFrame. Parameters like height and aspect set the size (in inches) of each subplot.


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import seaborn as sns
import matplotlib.pyplot as plt

# Loading the dataset with data about three different iris species
iris_df = sns.load_dataset('iris')

# Creating a pair plot
sns.pairplot(iris_df, height=2, aspect=0.8)

plt.show()

Hue

The hue parameter assigns colors based on a specified categorical column. This highlights group differences and, when used in classification datasets, shows how classes separate across variable pairs.

With hue set (e.g., to species), the scatter plots color points by class, and diagonal plots switch from histograms to KDE plots, making class distributions clearer.


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import seaborn as sns
import matplotlib.pyplot as plt

# Ignoring warnings
import warnings
warnings.filterwarnings('ignore')

# Loading the dataset with data about three different iris species
iris_df = sns.load_dataset('iris')

# Setting the hue parameter to 'species'
sns.pairplot(iris_df, hue='species', height=2, aspect=0.8)

plt.show()

Changing Plot Kinds

You can customize both the main and diagonal plots.

kind controls the off-diagonal plots (default: 'scatter');
diag_kind controls the diagonal (histogram or KDE, often chosen automatically when hue is used).


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import seaborn as sns
import matplotlib.pyplot as plt

# Loading the dataset with data about three different iris species
iris_df = sns.load_dataset('iris')

# Setting the kind parameter and diag_kind parameters
sns.pairplot(iris_df, hue='species', kind='reg', diag_kind=None, height=2, aspect=0.8)

plt.show()

'scatter', 'kde', 'hist', 'reg' are possible values for the kind parameter.

diag_kind can be set to one of the following values:

'auto';
'hist';
'kde';
None.

Everything is similar to the jointplot() function in this regard.

Study More

Explore more in the pairplot() documentation.

Task

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Use the correct function to create a pair plot.
Set the data for the plot to be penguins_df via the first argument.
Set 'sex' as the column which will map the plot aspects to different colors via specifying the second argument.
Set the non-diagonal plots to have a regression line ('reg') via specifying the third argument.
Set height to 2.
Set aspect to 0.8.

Solution

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Section 5. Chapter 6

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