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Create a 3D scatter plot in Matplotlib by making an axes with projection="3d", passing matching x, y, and z coordinates to ax.scatter(), and labeling all three axes. The example below uses repeatable sample data; replace it with your own values.
Make a basic 3D scatter plot
Install Matplotlib and NumPy in your Python environment if they are not already available. The following sample creates 100 illustrative points. The seed makes the sample data repeatable; it does not make the data meaningful for analysis.
import matplotlib.pyplot as plt
import numpy as np
rng = np.random.default_rng(42)
n = 100
x = rng.uniform(0, 10, n)
y = rng.uniform(0, 10, n)
z = rng.uniform(0, 10, n)
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.scatter(x, y, z)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
plt.show()This follows the pattern in the official Matplotlib 3D scatter example: create a 3D axes, plot the coordinates, label the axes, and show the figure.
Understand the coordinates and axes
fig.add_subplot(projection="3d") creates a 3D Axes object. You then call scatter() on that axes, rather than on pyplot. Matplotlib’s mplot3d tutorial describes this setup and the toolkit.
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In ax.scatter(x, y, z), values correspond by position: the first x, y, and z values define one point, the second values define another, and so on. The coordinate arrays should therefore have matching lengths. The Axes3D.scatter API also allows zs to be a single scalar, which places all points at the same z position; its default is 0.
If you already use the subplots convenience function, this is an equivalent way to create the axes:
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fig, ax = plt.subplots(subplot_kw={"projection": "3d"})Use fig.add_subplot() when adding an axes to a figure directly; use plt.subplots() when it fits the structure of the surrounding plotting code. In current Matplotlib, you do not need to import Axes3D explicitly for either approach. That import became unnecessary with Matplotlib 3.2.0, although older examples may include it.
Encode another variable with color or marker size
A 3D scatter plot can show an additional variable through color or marker size. For a numeric value, map the z coordinate to a colormap and include a colorbar to explain the scale:
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points = ax.scatter(x, y, z, c=z, cmap="viridis", s=30)
fig.colorbar(points, ax=ax, label="Z value")Here, c=z maps each point’s numeric z value to a color. cmap selects the color map; s sets marker area in points squared and may be a scalar or an array of per-point sizes. The API also supports color normalization through norm. See the scatter API reference for accepted forms and options.
For categories, use distinct colors or marker shapes and provide an explicit legend. When plotting several groups, choose encodings that remain distinguishable without relying on depth alone. The depthshade option shades points to suggest depth; Matplotlib applies it independently to each scatter call, so inspect the combined appearance of separately plotted groups.
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Place 2D data on a plane
The zdir parameter is useful when you want to position 2D coordinates on a plane in the 3D axes. For example, zdir="y" places the supplied x and z data on the x-z plane, with the fixed zs value specifying the position along y. Consult the API reference for the parameter details.
Rotate the view and check whether 3D helps
Matplotlib’s mplot3d toolkit draws a 3D scene as a 2D projection. The toolkit documentation describes it as a simple plotting toolkit, notes that it is less mature than Matplotlib’s 2D plotting, and says it is not the fastest or most feature-complete 3D library. In practice, points can overlap in the projected view, and an angle may hide relationships or make distances hard to judge.
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- Rotate the plot in an interactive window to inspect points from more than one angle.
- Check that each axis is labeled and that the displayed scales match the question you are trying to answer.
- Use an interactive Matplotlib backend for mouse rotation and zoom. The toolbar’s pan and zoom buttons do not work in the same way for 3D plots as they do for 2D plots; see the Matplotlib interactivity guide.
- If precise comparisons matter, consider whether separate 2D scatter plots communicate the relationships more clearly than a single 3D projection.
Version-specific scatter options
Matplotlib’s stable API documentation labels the current reference 3.11.2. Two optional parameters are version-sensitive: axlim_clip, which hides points outside the view limits, was added in Matplotlib 3.10; depthshade_minalpha was added in 3.11. Do not use these options in code that must run on earlier releases unless you have confirmed the installed version supports them. The additions are noted in the Axes3D.scatter API reference.
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