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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesFor most new Matplotlib figures, start with layout="constrained". It adapts spacing as the figure is drawn and is better suited to colorbars, nested subfigures, and axes spanning multiple rows or columns. Use tight_layout() when a simpler figure needs a one-time spacing adjustment with direct padding controls. Do not call tight_layout() after enabling constrained layout: it turns the constrained layout engine off.
How the two layout options differ
Both options adjust subplot spacing to help prevent labels and other decorations from colliding or being cut off, but they work differently. Matplotlib describes TightLayoutEngine as its first layout engine and ConstrainedLayoutEngine as the more modern built-in engine that generally gives better results. Constrained layout is “substantially more flexible,” according to the Matplotlib constrained layout guide.
| Option | How it works | Best suited to |
|---|---|---|
layout="constrained" |
Runs during figure draws and adjusts axes to accommodate supported decorations. | New figures, complex subplot arrangements, and figures with colorbars or nested subfigures. |
tight_layout() |
Makes a direct adjustment to spacing around subplots. | Simpler figures that need a one-time spacing adjustment and explicit padding controls. |
These descriptions reflect the Matplotlib layout engine API. Neither option guarantees correct placement for every custom artist, so inspect the rendered figure.
When to choose constrained layout
Choose constrained layout when you want Matplotlib to keep making room for supported decorations as it draws the figure, especially when the subplot structure is more involved than a basic grid. The guide documents support for colorbars associated with multiple axes, nested subfigures, and axes spanning rows or columns. It also tries to align spines in shared rows or columns.
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For a simple grid with fixed-aspect axes, compressed layout may reduce excess whitespace. The constrained-layout engine exposes a compress option for this case.
Enable it when creating the figure
Activate the engine before adding axes. For example:
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import matplotlib.pyplot as plt
fig, axs = plt.subplots(2, 2, layout="constrained")You can also enable it globally with rcParams['figure.constrained_layout.use'] = True. The creation-time approach is documented in the constrained layout guide.
When tight_layout is a good fit
For an existing, straightforward figure, call fig.tight_layout() to adjust the padding between and around subplots. Its padding controls are expressed as fractions of the font size, rather than inches. pad controls padding around the figure; h_pad and w_pad control vertical and horizontal spacing. The normalized rect parameter specifies the rectangle within which the subplot area should fit.
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For example, to leave space for a figure-level element above the subplot area, supply a normalized rectangle that ends below it:
fig.tight_layout(rect=(0, 0, 1, 0.92))If an Axes artist such as a legend or annotation should not affect the bounding-box calculation, call artist.set_in_layout(False). See the Figure.tight_layout API reference for the method’s parameters.
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Padding controls are not equivalent
The two engines offer different adjustment controls. Tight layout expresses its padding in font-size fractions; constrained layout uses inches for h_pad and w_pad, and fractions of figure size for hspace and wspace. It also supports a normalized rect and the compress option.
The API reference lists default values of 0.04167 inches for constrained-layout padding and 1.08 font-size fractions for tight-layout pad. These are configuration defaults, not measures of speed or quality; the units differ, so the values are not directly comparable. Consult the layout engine API and tight_layout API reference for details.
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Avoid mixing the two methods
Do not use fig.tight_layout() after turning on constrained layout. Matplotlib’s guide explicitly warns that calling tight_layout() turns constrained layout off. Pick the layout approach you want and adjust that engine’s settings instead.
Check the rendered figure for edge cases
Constrained layout accounts for tick labels, axis labels, titles, and legends, but other artists can still overlap or be clipped. In particular, the guide warns about artists positioned in Axes coordinates beyond the Axes boundary; it suggests adding such an artist directly to the Figure. Figures can also lay out poorly if pyplot.subplot calls use different row and column geometries. Font-rendering differences between backends may cause small output differences.
The constrained engine normally updates axes positions on each draw. If you need to keep positions stable after an initial draw—for example, because tick labels change during an animation—the guide shows disabling later updates with fig.set_layout_engine('none'). On backends with a toolbar, constrained layout is turned off during toolbar zoom and pan events. Review the constrained layout guide and inspect the final output, particularly when using custom artists or unusual subplot geometries.
Version and documentation note
The cited stable documentation identifies the layout-engine API and constrained-layout guide as Matplotlib 3.11.2 documentation, while the configuration page and Figure.tight_layout reference identify themselves as 3.11.0. Check the current stable documentation if you are working with a different Matplotlib version, since APIs and guidance can change.
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