4.4.4 Pairplot

A pair plot pairwise relationships in a dataset. The pair plot function creates a grid of Axes such that each variable in data will be shared in the y-axis across a single row and in the x-axis across a single column. The diagonal Axes are treated differently, drawing a plot to show the univariate distribution of the data for the variable in that column.

import seaborn as sns
plt.rcParams.update({'font.size': 18,'figure.figsize':(8, 6)})
tips = sns.load_dataset("tips")

Head of Tips Dataset

Let's draw a simple scatterplot for joint relationships and univariate distribution.

sns.pairplot(tips)

If you have a large dataset, you may consider a "small and specific" method to illustrate a subset of variables. Then we can use vars. Compare to the whole dataset, below only shows the relationship between tip and table size.

sns.pairplot(tips, vars=["tip", "size"])

Considering the simplicity, it's possible to remove the repetitive part and leave a lower triangle of bivariate axes.

sns.pairplot(tips,corner = True)

If you want to represent an additional level of conditionalization. Then we can use the parameterhue , which plots different subsets of data in different colors. Here is an example of showing different levels of a categorical variable by colors.

sns.pairplot(tips, kind="reg",hue = 'smoker',palette = 'husl')

Also, we can use differentmarkersfor each level of the hue variable, to illustrate the difference.

sns.pairplot(tips, kind="scatter",hue ='sex',markers=["o", "s"],palette = 'husl')