While pairplot() and PariGrid() enable plotting relationships between many variables in a grid of two variables each, matrix plots enable this in matrix format, using an aggregated metric relating the variables, such as correlation, covariance, or it could be normal business data such as finance, sales, or operations related to the two variables.

Seaborn provides two matrix plots, heatmap() and clustermap().

Heatmap() provides a colored representation of numbers to understand increasing, decreasing, diverging, or converging trends, which may not be easy to capture with numbers directly, especially when the numbers are too small or too large.

Clustermap() uses hierarchical clustering methods and plots the resulting dendrogram.