Matplotlib has two separate plot backgrounds: the Axes area inside the x- and y-axis region, and the Figure canvas around it. Set ax.set_facecolor() for the Axes, fig.set_facecolor() for the Figure, and choose a save-time color or transparency when exporting.
Change the Axes background or the Figure background
The Axes is the region where the data, ticks, and grid appear. The Figure is the larger canvas that contains the Axes and any surrounding space. Each has its own face color, so changing one does not necessarily change the other. Matplotlib’s configuration reference identifies axes.facecolor and figure.facecolor as distinct settings.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 3])
ax.set_facecolor("lightblue") # Area inside the Axes
fig.set_facecolor("lightgray") # Figure canvas around the Axes
plt.show()Color only the plotting area
Use ax.set_facecolor(color) when you want to fill the Axes interior without changing the surrounding Figure canvas.
ax.set_facecolor("#eef6ff")Color the canvas around the Axes
Use fig.set_facecolor(color) to change the Figure patch, including the space outside the Axes. The Figure API documents this setter.
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fig.set_facecolor("#fff4e6")Set both backgrounds
For a coordinated design, assign each area its own color. Check that tick labels, axis labels, grid lines, and plotted series remain easy to distinguish against the fills.
fig.set_facecolor("#222222")
ax.set_facecolor("#333333")Use a color name, hex code, or other supported color value
Matplotlib accepts several color formats, including named colors, hexadecimal strings, RGB tuples, and grayscale values. For example, "lightblue" and "#eef6ff" are both valid ways to specify a color; see the customization guide for color configuration details.
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Set background defaults for later plots
To change defaults for figures created afterward in the current session, assign the corresponding rcParams values:
import matplotlib.pyplot as plt
plt.rcParams["figure.facecolor"] = "#fff4e6"
plt.rcParams["axes.facecolor"] = "#eef6ff"Use plt.rc_context({...}) when a setting should apply only within a limited block, or configure a matplotlibrc file or style for reusable defaults. Matplotlib describes these configuration mechanisms in its customization documentation.
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Control the background when saving
A saved image can have a different background from the interactive figure. Specify facecolor in savefig when the export needs a particular solid fill; use transparent=True when the destination should show through the image instead.
fig.savefig("plot.png", facecolor="white")
fig.savefig("plot-transparent.png", transparent=True)The savefig API documents both controls. The configuration reference lists savefig.facecolor with a default of "auto" and savefig.transparent with a default of False. A transparent export is not the same as choosing a visible background color.
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Troubleshoot a background that did not change
- The area inside the axes is still white: You may have changed only
figure.facecolor. Setax.set_facecolor(...)oraxes.facecolorfor the Axes interior. - The area around the plot is still white: Change the Figure with
fig.set_facecolor(...)orfigure.facecolor. - The exported file looks different from the window: Set
facecolordirectly infig.savefig(...), or usetransparent=Trueif transparency is what you need.
The examples use the stable Matplotlib documentation, currently labeled version 3.11.2. If you need an exact signature or default for a particular installation, consult the documentation for that Matplotlib version.
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