First, use matplotlib and mpl_toolkits.mplot3d to draw a 3D surface diagram. The specific steps are: 1. Use np.linspace and np.meshgrid to create two-dimensional mesh data; 2. Calculate the corresponding Z value; 3. Create the ax.plot_surface to draw the surface and set the color map; 5. Add title, axis labels and color bars; 6. Use plt.show() to display the figure, which can be extended to 3D scatter plots, curve charts, and bar charts.
Here is a simple example of using Python's matplotlib
to draw a 3D surface plot. This example uses the mpl_toolkits.mplot3d
module to support 3D drawing.

? Basic 3D surface diagram example
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Required for 3D plotting # Create data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) Z = np.sin(np.sqrt(X**2 Y**2)) # Example function: Radial Sine# Create 3D image fig = plt.figure(figsize=(8, 6)) ax = fig.add_subplot(111, projection='3d') # Draw surface surf = ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='none', alpha=0.9) # Add title and tag ax.set_title('3D Surface Plot of sin(r)') ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') # Add color bar fig.colorbar(surf, ax=ax, shrink=0.5, aspect=10) # Show the graphics plt.show()
? illustrate
-
np.meshgrid
: Converts one-dimensional x and y into a two-dimensional grid to calculate the z-value corresponding to each (x, y) point. -
projection='3d'
: Enable 3D axis. -
plot_surface
: Draws a 3D surface,cmap
controls color mapping. -
alpha
: Controls transparency (optional). -
colorbar
: Displays the color scale corresponding to the height (Z value).
? Other common 3D graph types (blue examples)
1. 3D Scatter Plot
ax.scatter(X.flatten(), Y.flatten(), Z.flatten(), c=Z.flatten(), cmap='coolwarm', s=10)
2. 3D curve graph (Line Plot)
t = np.linspace(0, 10, 100) x = np.sin(t) y = np.cos(t) z = t ax.plot(x, y, z, label='3D helix') ax.legend()
3. 3D Bar Plot
x = y = np.arange(4) xx, yy = np.meshgrid(x, y) x, y = xx.ravel(), yy.ravel() z = np.zeros_like(x) dx = dy = 0.5 * np.ones_like(x) dz = np.random.rand(16) ax.bar3d(x, y, z, dx, dy, dz, shade=True)
? Tips
- Make sure
matplotlib
is installed:pip install matplotlib
- Although
Axes3D
imports can sometimes be omitted in new versions, explicit imports are safer. - The viewing angle (
ax.view_init(elev, azim)
elev, azim).
Basically that's it. This example is enough to help you start using Matplotlib for 3D visualization. Not complicated but it is easy to ignore details, such as meshgrid
and projection='3d'
.
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