Using Seaborn charts in Matplotlib multi-chart layouts

Seaborn basics

1 min read

Published Oct 7 2025, updated Aug 17 2026

ChartsGraphsMatplotlibNumPyPandasPythonSeabornVisualisation

Seaborn has two types of charts. Some are Axes level charts and some are Figure level charts.

Type	Description	Examples
Axes-level	Draw on a specific Matplotlib `Axes` — you can place them in subplots or dashboards	`sns.scatterplot()`, `sns.lineplot()`, `sns.barplot()`, `sns.boxplot()`, `sns.violinplot()`, `sns.heatmap()`, etc.
Figure-level	Create their own entire Figure (often with multiple subplots/facets) — not easily embedded in an existing layout	`sns.relplot()`, `sns.catplot()`, `sns.displot()`, `sns.lmplot()`, `sns.pairplot()`, `sns.jointplot()`, `sns.clustermap()`

Axes-level plots return a Matplotlib Axes object, meaning you can:

Combine them into multi-plot layouts
Add annotations, titles, or shared axes
Overlay or compose them with other Matplotlib plots

Example:

import seaborn as snsimport matplotlib.pyplot as plttips = sns.load_dataset("tips")fig, axs = plt.subplots(2, 2, figsize=(10, 8))sns.scatterplot(data=tips, x="total_bill", y="tip", ax=axs[0, 0])sns.boxplot(data=tips, x="day", y="total_bill", ax=axs[0, 1])sns.violinplot(data=tips, x="smoker", y="tip", ax=axs[1, 0])sns.heatmap(tips.corr(numeric_only=True), annot=True, ax=axs[1, 1])plt.tight_layout()plt.show()

Each subplot is a Seaborn plot embedded into a Matplotlib dashboard. You can freely mix Seaborn and Matplotlib plots.

Using Seaborn charts in Matplotlib multi-chart layouts

Seaborn basics

1 min read

Published Oct 7 2025, updated Aug 17 2026

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