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Use ax.set_facecolor() to change the background inside a plot’s axes, and fig.set_facecolor() to change the surrounding figure canvas. When exporting, set the background with savefig(facecolor=...) or use transparent=True for transparency.
Axes background vs. figure background
A Matplotlib figure can show two separate background areas: the Axes rectangle containing the plotted data and the larger Figure canvas around it. They have separate settings, so changing one does not necessarily change the other. The configuration reference lists axes.facecolor and figure.facecolor independently; both default to white in the documented configuration. Matplotlib configuration reference
- Axes interior: the plotting area inside the x- and y-axes. Set it with
ax.set_facecolor(color). - Figure canvas: the area around the Axes. Set it with
fig.set_facecolor(color).
Change the background for one plot
Use the object-oriented API returned by plt.subplots() to set either area, or both. The Figure API provides set_facecolor(color) for its rectangle. Matplotlib Figure API
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 3])
ax.set_facecolor("#eef6ff") # Axes interior
fig.set_facecolor("#fff4e6") # Figure canvas
plt.show()To change only the Axes, omit the fig.set_facecolor() line; to change only the canvas, omit ax.set_facecolor(). When choosing a custom color, check that labels, ticks, grid lines, and data series remain visible against it.
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Choose a color value
Matplotlib accepts several color representations, including named colors, quoted hexadecimal strings, RGB tuples, and grayscale values. The customization guide’s color section
ax.set_facecolor("lightblue")
ax.set_facecolor("#eef6ff")
ax.set_facecolor((0.9, 0.95, 1.0))Set background defaults for later plots
To change defaults for figures created later in the current session, assign the corresponding rcParams:
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import matplotlib.pyplot as plt
plt.rcParams["figure.facecolor"] = "#fff4e6"
plt.rcParams["axes.facecolor"] = "#eef6ff"These assignments set session-wide defaults. For a change limited to a block of code, use plt.rc_context(); for reusable defaults, Matplotlib also supports configuration through a matplotlibrc file or a style configuration. Matplotlib customization and configuration matplotlibrc configuration
with plt.rc_context({
"figure.facecolor": "#fff4e6",
"axes.facecolor": "#eef6ff",
}):
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [2, 4, 3])Control the background when saving
A saved image can need a different background from the interactive window. Set the export’s face color explicitly when the file should have a solid background, or request transparency when the image should let a page or document show through. Matplotlib documents savefig’s facecolor option and the savefig.facecolor setting, whose default is auto; savefig.transparent defaults to False. Matplotlib savefig API Matplotlib configuration reference
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fig.savefig("plot.png", facecolor="white")
fig.savefig("plot-transparent.png", transparent=True)Use transparent=True when transparency, rather than a visible solid fill, is the intended result. If the saved file does not match the display, check the save-time options and relevant savefig defaults.
Fix common background mismatches
The plot area is still white
If only the canvas changed, the Axes retains its own face color. Set ax.set_facecolor(...) as well if the interior plotting rectangle should change.
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The exported image looks different from the window
Set the desired export background directly in fig.savefig(..., facecolor=...), or check the savefig face-color and transparency settings.
The image should have no baked-in solid background
Save with transparent=True. Transparency is an export choice, not a color value.
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Version note
The linked stable documentation is labeled Matplotlib 3.11.2. If an exact signature or default matters for your installation, consult the documentation for that installed version.
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