Direct comparison
Seaborn vs Matplotlib: What Each Does
Seaborn vs matplotlib compared: how seaborn draws with matplotlib, axes-level vs figure-level, OO vs pyplot, and when to use each for figures.
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How do Seaborn, Matplotlib compare side by side?
The table below compares Seaborn, Matplotlib across 9 procurement-relevant dimensions, from relationship through source.
Side-by-side comparison
| Dimension | Seaborn | Matplotlib |
|---|---|---|
| Relationship | Builds on top of matplotlib and integrates closely with pandas data structures; it uses matplotlib to draw its plots. | The underlying drawing library that seaborn calls behind the scenes. |
| Interface levels | Two levels: axes-level functions and figure-level functions. | Two interfaces: the explicit Figure/Axes object-oriented (OO) style and the implicit pyplot style. |
| What a plotting call returns | Axes-level functions return the matplotlib Axes they drew on; figure-level functions return a FacetGrid. | In the OO style you work with Axes objects that belong to a Figure; a Figure contains one or more Axes. |
| Multiple subplots | Figure-level functions make multiple subplots easy, for example splitting by a variable with col="species". | A Figure can hold one or more Axes, which you arrange yourself. |
| Customization | Full customization of a graphic requires some knowledge of matplotlib's concepts and API. | The OO style is recommended for complicated plots and for functions and scripts meant to be reused. |
| Interactive use | Outside an IPython/Jupyter matplotlib mode you have to call matplotlib.pyplot.show() to display a plot. | The pyplot style can be very convenient for quick interactive work. |
| Saving figures | Drawing is done by matplotlib, so the matplotlib figure is what gets saved. | savefig(fname, ...) writes the figure; the available output formats depend on the backend in use, and dpi defaults to 'figure'. |
| When to pick each | Pick seaborn to explore data in a pandas DataFrame and to facet a plot across subsets with a figure-level function. | Pick matplotlib (OO style) when you need control over the final figure; use it alongside seaborn when a seaborn plot needs finer adjustment. |
| Source | seaborn documentation, introduction and function overview (seaborn 0.13.2 docs), read 2026-10-29. | matplotlib documentation, Quick start guide and pyplot.savefig reference (stable), read 2026-10-29. |
Common questions
Common questions about Seaborn vs Matplotlib
Does seaborn replace matplotlib?
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No. Seaborn uses matplotlib to draw its plots, and its documentation notes that full customization of a seaborn graphic requires some knowledge of matplotlib's concepts and API.
What does a seaborn function return?
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Axes-level functions return the matplotlib Axes object they plotted on. Figure-level functions return a FacetGrid, which interfaces with matplotlib through a seaborn object.
Why does my seaborn plot not appear in a script?
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Outside an IPython/Jupyter matplotlib mode, seaborn's documentation says you have to call matplotlib.pyplot.show() to display the plot.
Which matplotlib interface should I use?
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The matplotlib Quick start suggests the object-oriented style for complicated plots and for reusable functions and scripts, and describes the pyplot style as very convenient for quick interactive work.
Going deeper
