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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Use ax.scatter(x, y) to plot paired values, then call fig.tight_layout() after adding the title and axis labels for a simple, one-time spacing adjustment. For plots with legends, colorbars, or more complex layouts, create the figure with layout="constrained" instead; do not call tight_layout() afterward.
Make a scatter plot and adjust its layout
This example plots five paired observations, adds labels and a title, then asks Matplotlib to adjust subplot spacing:
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import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 1, 4, 3, 5]
fig, ax = plt.subplots()
ax.scatter(x, y, s=40, color="tab:blue", alpha=0.8)
ax.set_xlabel("X value")
ax.set_ylabel("Y value")
ax.set_title("Example scatter plot")
fig.tight_layout()
plt.show()x and y give the horizontal and vertical positions of the points. The s argument sets marker area in typographic points squared, not its radius. color assigns one color to all points, and alpha controls transparency.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCall fig.tight_layout() after adding plot decorations so Matplotlib can adjust spacing around the Axes. It adjusts the figure when called; it does not normally recalculate layout on every redraw. The official Matplotlib tight layout guide describes automatic behavior as something you can enable separately with fig.set_tight_layout(True) or the figure.autolayout setting.
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Encode another variable with marker color or size
Map numeric values to color
To show a third numeric variable, pass its values as c. A colormap maps those values to colors; use cmap to select the map and norm when you need control over how values are normalized:
fig, ax = plt.subplots()
points = ax.scatter(x, y, c=[10, 20, 30, 40, 50], cmap="viridis")
ax.set_xlabel("X value")
ax.set_ylabel("Y value")
ax.set_title("Scatter plot colored by a third variable")
fig.colorbar(points, ax=ax, label="Third variable")
fig.tight_layout()The colorbar explains how the numeric values correspond to color. Because colorbars add layout complexity, constrained layout is often a better choice for this version.
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Vary marker size
Supply a sequence to s to vary marker area for each point. Since the values are in points squared, choose them as areas rather than treating them as radii. The Matplotlib scatter API documents the available marker, size, color, transparency, edge, and colormap options.
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Choose an unambiguous color argument
Use color="tab:blue" for a uniform color. The c argument can mean a color specification, per-point colors, numeric values for colormapping, or an RGB(A) array, so a single numeric RGB(A) sequence can be ambiguous with data values. The scatter API recommends color= when all points should share one color.
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Marker edges can also change how large points appear: edge linewidth is centered on the marker boundary, so a positive linewidth can make small markers look larger. To remove edges, use linewidths=0 or edgecolors="none".
Choose between tight layout and constrained layout
| Layout choice | How to activate it | What it handles | Best fit |
|---|---|---|---|
tight_layout |
Call fig.tight_layout() after adding plot elements. |
Primarily tick labels, axis labels, and titles. | Simple figures needing a one-time spacing adjustment. |
| Constrained layout | Create the figure with fig, ax = plt.subplots(layout="constrained"). |
Labels and titles, plus elements such as legends and colorbars; supports more complex grids. | Figures with multiple Axes or more involved arrangements. |
Matplotlib’s constrained layout guide recommends enabling it when creating the figure, before adding Axes. For example:
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fig, ax = plt.subplots(layout="constrained")
ax.scatter(x, y)
ax.set_xlabel("X value")
ax.set_ylabel("Y value")
ax.set_title("Example scatter plot")
plt.show()Do not follow this with fig.tight_layout(): calling tight layout turns constrained layout off. Matplotlib’s tight layout guide says the more modern and capable constrained layout should typically be used instead.
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Check the rendered figure when text is still cut off
tight_layout is not a guarantee against clipping. Its documented extent checks focus on tick labels, axis labels, and titles, and the guide notes that it may not work in every case. Some artists can be excluded from layout calculations with Artist.set_in_layout, which may affect the available spacing.
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- Inspect the displayed figure or the saved output; layout results can depend on the figure’s decorations and geometry.
- Avoid setting
pad=0if text is close to the edge. The guide warns that zero padding can clip text by a few pixels and recommends padding greater than 0.3. - If you call tight layout repeatedly, small variations are possible because the algorithm does not necessarily converge.
- For a crowded plot with a legend, colorbar, or multiple Axes, try constrained layout from figure creation instead of relying on a later tight-layout adjustment.
For extra separation with the simpler layout, fig.tight_layout(pad=1.2) increases padding; w_pad and h_pad control horizontal and vertical spacing. These padding values are fractions of the font size, as documented in the tight layout guide. Check the actual output after changing them.
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