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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To plot multiple 3D lines in Matplotlib, create an axes with projection="3d", then call ax.plot(x, y, z) once for each trajectory. Use Line3DCollection instead when your lines are already grouped as segments or have different numbers of points.
Plot multiple 3D trajectories with ax.plot
Matplotlib’s documented setup is to pass projection="3d" to Figure.add_subplot. The resulting axes provides the 3D plot method. In each call, the three coordinate arrays supply the x, y, and z values for one line; keep the arrays for that trajectory aligned.
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
t = np.linspace(0, 2 * np.pi, 200)
ax.plot(np.cos(t), np.sin(t), t, label="helix")
ax.plot(np.cos(t), np.sin(t), 0.5 * t, label="second line")
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
ax.legend()
plt.show()Each ax.plot call adds a trajectory to the same axes. Give lines meaningful labels and, when they are difficult to distinguish, use different styles or colors. The example applies the documented Axes3D.plot API; the number of points and equations are illustrative, not performance recommendations.
When to use Line3DCollection
A line collection can be a better fit when your data is already organized as a group of line segments, or when trajectories have unequal point counts. The Line3DCollection API supports 3D lines with different numbers of points and styling such as colors and line widths. Add the collection to the 3D axes using its collection API, rather than making a separate ax.plot call for every trajectory.
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Choose based on the shape of your input and the control your code needs: individual plotting calls make separate trajectories easy to read and label, while a collection keeps grouped segments together. The documentation does not establish a general speed advantage for collections, so do not assume one without benchmarking your own data and Matplotlib version.
What Matplotlib’s 3D display means
Matplotlib describes mplot3d as a toolkit that supplies an axes object to “create a 2D projection of a 3D scene.” Your data has three coordinates, but the figure displays a projected view of that scene. Matplotlib presents mplot3d as a simple 3D plotting toolkit, not a full-featured 3D rendering system. Its overview says it is “Not the fastest or most feature complete 3D library out there,” and the API documentation notes that 3D plotting is “still not as mature as the 2D case.” It is convenient for straightforward plots; for complex or performance-sensitive 3D work, evaluate whether another tool better fits the requirements.
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Rotate and zoom in an interactive figure
With a supported interactive backend, you can inspect the plotted trajectories by rotating and zooming the view: the mplot3d view-angle guide describes click-drag rotation and right-click-drag zoom, as well as middle-button panning. These controls depend on the interactive backend; they are not features of a static image such as a saved PNG.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check your Matplotlib version if behavior matters
The relevant stable tutorial and API pages identify Matplotlib 3.11.2, while the versioned Line3DCollection reference is for 3.11.0. If a method signature or behavior is important to your code, check the documentation matching your installed Matplotlib version.
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