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Change the marker size in a Matplotlib 3D scatter plot with the s argument on Axes3D.scatter:
ax.scatter(x, y, z, s=40)The value is measured in typographic points squared, not pixels and not units from your x, y, or z data. A scalar gives every point the same size; an array-like value lets each point have its own size. The documented default is s=20 in the Matplotlib 3.11.2 API.
The smallest edit to an existing 3D scatter plot
If your plot already has a three-dimensional axes object named ax, add s directly to the scatter call:
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For example, this changes a call that previously used the default:
# Before: uses the API default, s=20
ax.scatter(x, y, z)
# After: every marker uses the supplied size
ax.scatter(x, y, z, s=40)When you redraw the figure, the new collection uses the requested size. If the plot is built inside a loop, put s on the call in that loop so every newly created collection receives the intended value.
Complete setup for a new 3D scatter plot
A 3D scatter plot needs an axes object configured with the 3D projection. The official Matplotlib 3D scatter gallery example uses fig.add_subplot(projection='3d') and passes x, y, and z coordinates to scatter.
import matplotlib.pyplot as plt
# Example coordinate data
x = [1, 2, 3, 4, 5]
y = [2, 1, 4, 3, 5]
z = [0, 3, 1, 4, 2]
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.scatter(x, y, z, s=40)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
plt.show()The important parts are the 3D projection when the axes is created and the three coordinate sequences in ax.scatter(x, y, z, ...). The labels are optional and do not affect marker size.
What the s value actually means
Matplotlib documents marker size as “points2.” A point is a typographic unit, so s=40 does not mean a 40-pixel diameter, a 40-pixel radius, or a 40-unit square in your data coordinate system. It is an area-like display value used by the marker collection.
This distinction matters when you compare plots. A marker with s=40 may occupy a different number of screen pixels when you change the figure size, display resolution, export dimensions, or viewing context. The API defines the unit but does not prescribe one universally ideal value for every figure. Treat example values as starting points and adjust them while looking at the final output.
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With no s argument, the documented default is 20 for the Matplotlib 3.11.2 Axes3D.scatter signature. Supplying a value replaces that default for the call.
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Choose one size for all points or a size for each point
| Goal | Value passed to s |
Example | Requirement |
|---|---|---|---|
| Uniform markers | One scalar number | s=40 |
Every plotted point receives the same display size. |
| Variable markers | An array-like of numbers | s=[10, 20, 40, 80, 120] |
Provide one size for each plotted point, in the same order as the coordinate data. |
| Default behavior | Omit s |
ax.scatter(x, y, z) |
Uses the API default of 20 for the cited Matplotlib version. |
Uniform marker size with a scalar
Use a scalar when marker area is only a visual choice and has no meaning in the data:
ax.scatter(x, y, z, s=15)All points use the same size, including points that overlap in the projected view. A scalar is also the simplest option when you want to tune readability by changing one number.
Point-by-point sizes with an array-like
Pass a list, tuple, or NumPy array when marker size should vary:
sizes = [12, 24, 36, 60, 90]
ax.scatter(x, y, z, s=sizes)The first size belongs to the first x, y, and z coordinates, the second size to the second coordinates, and so on. Keep the size sequence aligned with the data after filtering, sorting, or otherwise rearranging points.
With NumPy, the same pattern can be expressed as an array:
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import numpy as np
sizes = np.array([12, 24, 36, 60, 90])
ax.scatter(x, y, z, s=sizes)The API describes s as array-like, so it is not limited to a particular container type. The practical rule is that the array must provide a corresponding value for every point being plotted.
Deriving sizes from another measurement
If another variable controls visual prominence, transform it into positive display values before passing it to s. For example:
importance = np.array([0.2, 0.5, 0.8, 1.1, 1.6])
sizes = 20 + importance * 40
ax.scatter(x, y, z, s=sizes)The multiplication and offset in this example are design choices, not a Matplotlib calibration. They simply keep the values away from zero while preserving the ordering of the importance values. Inspect the result and adjust the transformation for your figure.
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Practical size values and visual tuning
There is no documented “best” value that looks correct for every 3D chart. Start with a scalar such as 20, 40, or 80, then check the actual figure at the size in which readers will see it. A value that is legible in an interactive window can look crowded after a camera angle change or when many points project onto the same region.
- Use a smaller value when dense points obscure one another.
- Use a larger value when a sparse plot is difficult to see or when the figure will be displayed at a small physical size.
- When sizes encode a variable, preserve the ordering of values and avoid a range so wide that small points disappear.
- Remember that the number is in points squared; do not describe it as a pixel diameter or as a distance in x, y, or z units.
Because 3D scenes are projected onto a 2D display, apparent marker size also depends on overlap and viewpoint. A point farther back in the scene is still rendered by the same size parameter, but the projection can make visual comparisons less intuitive than in a flat chart.
Using the argument with common scatter options
s can be supplied alongside normal scatter options such as color and transparency:
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ax.scatter(
x,
y,
z,
s=50,
c=[0.1, 0.4, 0.7, 0.8, 1.0],
cmap="viridis",
alpha=0.8,
)These options control different properties. s controls marker size, c and cmap control coloring, and alpha controls transparency. Changing color or transparency will not change the size value, so keep s explicit when size is important to your chart.
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Many scripts create the axes in one statement and call scatter later. You only need to edit the latter:
fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(111, projection="3d")
# Existing data and styling remain unchanged
ax.scatter(x, y, z, s=65, color="steelblue", alpha=0.75)
plt.show()The positional form add_subplot(111, projection="3d") and the shorter add_subplot(projection="3d") both create a 3D axes. The marker-size change is still made on ax.scatter, regardless of which subplot syntax your script uses.
Troubleshooting marker-size problems
The markers did not change
- Confirm that the edited line is the one that actually runs. In a script with several scatter calls, each call creates its own collection and must receive its own
svalue. - Check that
sis spelled with a lowercase letter and is inside thescatter(...)call. - Make sure an old figure is not being displayed instead of the figure created after the edit. Re-run the cell or script that constructs the axes and call
plt.show()after the updated scatter call.
An array of sizes raises an error or does not align
Verify that the size array has one entry for every point in the plotted data. If you remove rows from x, y, and z, apply the same filtering to the size array. If you sort the coordinates, reorder sizes with the same index order. A scalar avoids alignment issues when all markers should be identical.
The result is too small or too large
Change the numeric value and render the figure again. Because s is an area-like points-squared value, doubling the number does not mean a marker with exactly twice the visual diameter. Compare a few values in the final output rather than trying to convert the value to pixels.
The code creates a flat plot instead of a 3D plot
Inspect axes creation. It must include the 3D projection, for example:
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ax = fig.add_subplot(projection="3d")Then call the 3D axes method:
ax.scatter(x, y, z, s=40)If the axes was created as a normal 2D subplot, the 3D call pattern will not be available in the expected form.
Version and 3D-rendering considerations
The cited API page is the Matplotlib 3.11.2 stable documentation. Its signature shows Axes3D.scatter(xs, ys, zs=0, zdir='z', s=20, ...), with s accepting a float or array-like value. Defaults and signatures can change between releases, so check the documentation corresponding to the Matplotlib version installed in your environment if behavior differs.
Matplotlib’s mplot3d toolkit overview describes the toolkit as a 2D projection of a 3D scene and notes that 3D plotting is less mature than the 2D case. That limitation affects depth ordering and visual interpretation, but it does not remove support for the s argument. You can still set a uniform or per-point marker size on Axes3D.scatter.
A reusable pattern for both sizing modes
Use a parameter in your own plotting function when you want to switch between a uniform size and data-driven sizes without changing the rest of the chart:
import matplotlib.pyplot as plt
import numpy as np
def scatter_3d(x, y, z, sizes=40):
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.scatter(x, y, z, s=sizes)
return fig, ax
x = np.array([1, 2, 3, 4])
y = np.array([2, 4, 1, 3])
z = np.array([0, 1, 3, 2])
# One value for every point
fig, ax = scatter_3d(x, y, z, sizes=40)
plt.show()
# One value per point
fig, ax = scatter_3d(x, y, z, sizes=np.array([15, 30, 55, 90]))
plt.show()Here, sizes=40 is a scalar in the first call, while the second call supplies an array-like sequence. Both calls use the same 3D axes setup and coordinate data.
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Final checklist
- Add
s=valueto theax.scatter(x, y, z, ...)call. - Use a scalar for one size across all points.
- Use an array-like with one aligned value per point for variable sizes.
- Interpret values as points squared, not pixels or data units.
- Remember that omitting
suses the documented default of20for Matplotlib 3.11.2. - Create the axes with
projection="3d"when starting a new plot. - Tune the number against the final figure size and viewpoint instead of assuming one universal value.
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