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Create each 3D subplot with projection='3d', then plot through the 3D axes object Matplotlib returns. Repeat the call with a different subplot index for each panel. You can also mix 2D and 3D axes in the same figure.
Create a figure with two 3D subplots
Use Figure.add_subplot(rows, columns, index, projection='3d') for each 3D panel. The first two arguments define the grid; the third selects the panel, counting from 1.
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_subplot(1, 2, 1, projection='3d')
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
ax1.scatter([0, 1, 2], [0, 1, 0], [0, 1, 2])
ax2.plot([0, 1, 2], [0, 1, 1], [0, 1, 2])
plt.show()This creates one row with two columns: a 3D scatter plot on the left and a 3D line plot on the right. The returned ax1 and ax2 objects are separate axes, so each plot is added to its own panel.
Choose a plot method for each panel
Call plotting methods on the axes object, not on pyplot functions. The pyplot plotting functions have 2D signatures; 3D axes methods accept the extra coordinate or surface information. The mplot3d API documentation describes the 3D toolkit.
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ax.scatter(x, y, z)displays individual 3D points.ax.plot(x, y, z)draws a 3D line or trajectory.ax.plot_surface(X, Y, Z)displays gridded height data as a surface.ax.plot_wireframe(X, Y, Z)emphasizes the mesh structure of a surface.
For example, adjacent panels can show a surface and its wireframe representation. See the official multiple-3D-subplots gallery example.
Adapt the grid to your layout
Change the row and column counts to fit the number of panels, and assign a unique index to every axes. For a 2-by-2 grid, for example, use fig.add_subplot(2, 2, 1, projection='3d') through fig.add_subplot(2, 2, 4, projection='3d') to create four 3D axes. Choose figsize to give the panels room to be read; the dimensions are a presentation choice, not a requirement of 3D plotting.
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For comparisons, keep the meaning of axes, labels, and data ranges consistent where appropriate. If panels use color to represent values, use comparable color scales when readers need to compare those values; otherwise, each panel may communicate a different range.
Mix 2D and 3D axes in one figure
Omit the projection argument for a regular 2D subplot, and include projection='3d' only for the 3D one:
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fig = plt.figure(figsize=(8, 8))
ax2d = fig.add_subplot(2, 1, 1)
ax3d = fig.add_subplot(2, 1, 2, projection='3d')
ax2d.plot([0, 1, 2], [0, 1, 0])
ax3d.plot([0, 1, 2], [0, 1, 1], [0, 1, 2])
plt.show()The official mixed 2D/3D gallery example demonstrates this arrangement.
Set labels, limits, and colorbars
Configure each panel through its axes object: set labels and limits on the relevant axes, and attach a colorbar to the plotted artist when one is needed. The gallery example shows a surface subplot with a z-axis limit and a colorbar associated with the surface.
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Do you need to import mplot3d?
For current Matplotlib, no extra import is needed just to make projection='3d' available to add_subplot. The stable tutorial notes that explicitly importing mpl_toolkits.mplot3d for this purpose stopped being necessary in Matplotlib 3.2.0. Older examples may include that import, but it can generally be omitted for this use. See the stable 3D plotting tutorial.
Rotate or zoom a 3D subplot
With interactive Matplotlib backends, mouse gestures may let you rotate or zoom a 3D scene. The exact interaction depends on the backend, as noted in the mplot3d API documentation.
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