How to change plot background color?

Use the set_facecolor(color) method of the axes object, which you've created one of the following ways:

• You created a figure and axis/es together

  fig, ax = plt.subplots(nrows=1, ncols=1)
  

• You created a figure, then axis/es later

  fig = plt.figure()
  ax = fig.add_subplot(1, 1, 1) # nrows, ncols, index
  

• You used the stateful API (if you're doing anything more than a few lines, and especially if you have multiple plots, the object-oriented methods above make life easier because you can refer to specific figures, plot on certain axes, and customize either)

  plt.plot(...)
  ax = plt.gca()
  

Then you can use set_facecolor:

ax.set_facecolor('xkcd:salmon')
ax.set_facecolor((1.0, 0.47, 0.42))

As a refresher for what colors can be:

matplotlib.colors

Matplotlib recognizes the following formats to specify a color:

• an RGB or RGBA tuple of float values in [0, 1] (e.g., (0.1, 0.2, 0.5) or (0.1, 0.2, 0.5, 0.3));
• a hex RGB or RGBA string (e.g., '#0F0F0F' or '#0F0F0F0F');
• a string representation of a float value in [0, 1] inclusive for gray level (e.g., '0.5');
• one of {'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'};
• a X11/CSS4 color name;
• a name from the xkcd color survey; prefixed with 'xkcd:' (e.g., 'xkcd:sky blue');
• one of {'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'} which are the Tableau Colors from the ‘T10’ categorical palette (which is the default color cycle);
• a “CN” color spec, i.e. 'C' followed by a single digit, which is an index into the default property cycle (matplotlib.rcParams['axes.prop_cycle']); the indexing occurs at artist creation time and defaults to black if the cycle does not include color.

All string specifications of color, other than “CN”, are case-insensitive.

One method is to manually set the default for the axis background color within your script (see Customizing matplotlib):

import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'black'

This is in contrast to Nick T's method which changes the background color for a specific axes object. Resetting the defaults is useful if you're making multiple different plots with similar styles and don't want to keep changing different axes objects.

Note: The equivalent for

fig = plt.figure()
fig.patch.set_facecolor('black')

from your question is:

plt.rcParams['figure.facecolor'] = 'black'

Something like this? Use the axisbg keyword to subplot:

>>> from matplotlib.figure import Figure
>>> from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
>>> figure = Figure()
>>> canvas = FigureCanvas(figure)
>>> axes = figure.add_subplot(1, 1, 1, axisbg='red')
>>> axes.plot([1,2,3])
[<matplotlib.lines.Line2D object at 0x2827e50>]
>>> canvas.print_figure('red-bg.png')

(Granted, not a scatter plot, and not a black background.)

Simpler answer:

ax = plt.axes()
ax.set_facecolor('silver')

If you already have axes object, just like in Nick T's answer, you can also use

 ax.patch.set_facecolor('black')

The easiest thing is probably to provide the color when you create the plot :

fig1 = plt.figure(facecolor=(1, 1, 1))

or

fig1, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, facecolor=(1, 1, 1))

One suggestion in other answers is to use ax.set_axis_bgcolor("red"). This however is deprecated, and doesn't work on MatPlotLib >= v2.0.

There is also the suggestion to use ax.patch.set_facecolor("red") (works on both MatPlotLib v1.5 & v2.2). While this works fine, an even easier solution for v2.0+ is to use

ax.set_facecolor("red")

In addition to the answer of NickT, you can also delete the background frame by setting it to "none" as explain here: https://stackoverflow.com/a/67126649/8669161

import matplotlib.pyplot as plt
plt.rcParams['axes.facecolor'] = 'none'

I think this might be useful for some people:

If you want to change the color of the background that surrounds the figure, you can use this:

fig.patch.set_facecolor('white')

So instead of this:

you get this:

Obviously you can set any color you'd want.

P.S. In case you accidentally don't see any difference between the two plots, try looking at StackOverflow using darkmode.