Matplotlib treats the plotting area and the surrounding figure canvas as separate objects, so set each facecolor independently. Use ax.set_facecolor() for the inner Axes and fig.set_facecolor() for the outer Figure.
Change both backgrounds in one plot
In Matplotlib, the Figure is the outer container and each Axes is a plotting region inside it. The official background customization example shows both areas being set separately.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
fig.set_facecolor("lightblue") # outer Figure canvas
ax.set_facecolor("whitesmoke") # inner Axes plotting area
ax.plot([1, 2, 3], [2, 1, 3])
plt.show()The Figure API also supports fig.patch.set_facecolor(color); the Axes patch can likewise be changed with ax.patch.set_facecolor(color). The simpler object setters make the target region clear. Matplotlib documents the Figure as the top-level container for plot elements.
Color only the region you intend
ax.set_facecolor("whitesmoke")colors the Axes rectangle, not the Figure margins.fig.set_facecolor("lightblue")colors the Figure canvas; it does not by itself set each Axes background.- To make both regions the same color, call both setters with the same value, for example
"#f2f2f2".
Set backgrounds for multiple subplots
A Figure may contain several Axes. Set the shared outer color once, then set the facecolor on each Axes that needs a custom plotting-area color.
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fig, axes = plt.subplots(1, 2)
fig.set_facecolor("lightblue")
for ax in axes:
ax.set_facecolor("whitesmoke")Each Axes is an individual object, so changing one does not automatically change the others.
Choose defaults for future plots
For repeated charts, Matplotlib provides separate configuration settings: figure.facecolor for the Figure and axes.facecolor for Axes. Set these through rcParams or a style configuration; for a single plot, per-object setters keep the change local. See the Matplotlib configuration reference.
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import matplotlib as mpl
mpl.rcParams["figure.facecolor"] = "lightblue"
mpl.rcParams["axes.facecolor"] = "whitesmoke"Make the saved image match
Saving has its own options. The savefig API documents facecolor; its "auto" value uses the current Figure facecolor. You can also pass the desired color explicitly:
fig.savefig("plot.png", facecolor=fig.get_facecolor())For a transparent export, use transparent=True:
fig.savefig("plot.png", transparent=True)The configuration reference also documents savefig.transparent as a default setting. Transparency and explicit facecolor options can interact with the call arguments and Matplotlib version, so consult the savefig documentation for the version in use and inspect the exported file in its intended format.
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Documentation version
The linked stable documentation identifies Matplotlib 3.11.2. These setters and settings are documented there; labels and behavior can change in later releases, so use the documentation matching your installed version if an option behaves differently.
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