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Get the 3D axes object
Matplotlib’s mplot3d toolkit creates a 2D projection of a 3D scene. Configure the plot through its Axes3D object, rather than passing 3D-specific tick options to pyplot. The official mplot3d documentation describes the toolkit and its capabilities.
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.scatter([0, 1, 2], [10, 20, 30], [100, 200, 300])The examples below assume ax is this 3D axes object. Matplotlib’s 3D axes API reference documents the z-axis tick controls; corresponding x- and y-axis methods follow the same pattern.
Set tick positions on x, y, and z
Give each axis the numeric positions where you want ticks to appear:
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ax.set_xticks([0, 1, 2])
ax.set_yticks([10, 20, 30])
ax.set_zticks([100, 200, 300])Use the positions that make sense for the data and scale. The axis-specific methods set locations; labels are otherwise handled by the axis formatter.
Use custom tick labels
For arbitrary text, provide tick positions and labels together. There must be one label for each position:
ax.set_zticks([0, 1, 2], labels=["low", "middle", "high"])The labels are used as supplied. To customize another axis, apply the same approach with set_xticks or set_yticks. See the official set_zticks reference for its arguments and behavior.
Avoid calling set_zticklabels by itself when tick positions are not fixed. Matplotlib discourages setting labels independently because labels are associated with tick positions and can appear in the wrong places if those positions later change. When the default labels do not suit unusual positions or scales, an axis formatter is another option; for example, the default log formatter may label only its customary positions.
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Style tick marks and labels
Use tick_params to adjust tick appearance rather than styling only the current tick-label objects. For example, to change the z-axis label size:
ax.tick_params(axis="z", labelsize=10)Consult the current tick_params API reference for supported options. The pyplot signatures are strictly 2D; applying tick controls to the 3D axes object avoids relying on pyplot arguments for 3D content.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep exact axis limits
Setting ticks can expand an axis view limit so every requested tick is visible. If the displayed bounds must remain exact, set ticks first and then apply the desired limits:
ax.set_zticks([0, 1, 2])
ax.set_zlim(0, 2)For the other dimensions, use set_xlim or set_ylim after setting their ticks. This ordering lets the requested bounds take precedence over any limit expansion caused by adding ticks.
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A 3D Matplotlib axes displays a 2D projection, so tick spacing and label placement can look different as the viewing angle or projection changes. Matplotlib also cautions that 3D plotting is less mature than its 2D plotting. Check the final view after adjusting ticks, labels, or camera angle rather than assuming their screen layout will remain unchanged.
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