import matplotlib.pyplot as plt import numpy as np X = np.array([1, 2, 3]) Y = np.array([1, 2, 3]) Z = np.array([1, 2, 3]) fig = plt.figure() ax = fig.gca(projection='3d') surface = ax.plot_surface(X, Y, Z, linewidth=0, antialiased=False) plt.show()
The output when running this code is as follows
Warning (from warnings module): File "C:\Users\xcent\Desktop\code.py", line 10 ax = fig.gca(projection='3d') MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot(). Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 11, in <module> surface = ax.plot_surface(X, Y, Z, linewidth=0, antialiased=False) File "C:\Users\xcent\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\_api\deprecation.py", line 431, in wrapper return func(*inner_args, **inner_kwargs) File "C:\Users\xcent\AppData\Local\Programs\Python\Python39\lib\site-packages\mpl_toolkits\mplot3d\axes3d.py", line 1658, in plot_surface raise ValueError("Argument Z must be 2-dimensional.") 👉 ValueError: Argument Z must be 2-dimensional.🤔
The error message
ValueError: Argument Z must be 2-dimensional. indicates that the
plot_surface method requires a 2-dimensional array for the
Z parameter. In the code above, I’m passing a 1-dimensional array
Z = np.array([1, 2, 3]).
As for the deprecation warning, this is because the method
gca() will no longer accept keyword arguments in future versions of Matplotlib, so I should use a different method to create the axes.
Here’s a modified version of your code that should work correctly. This code creates a simple 3D surface where the Z values are simply the product of the X and Y values:
import matplotlib.pyplot as plt import numpy as np X = np.array([[1, 2, 3]]) Y = np.array([, , ]) Z = X * Y fig = plt.figure() ax = plt.axes(projection='3d') surface = ax.plot_surface(X, Y, Z, linewidth=0, antialiased=False) plt.show()
In the fixed code,
Z are all 2-dimensional arrays now. I made
Y 2D by adding an extra set of brackets around their definitions. This creates arrays with shapes
💡 Shape Example: A 2D array with shape
(1,3) is like a single row with 3 columns, containing 3 elements. An array with shape
(3,1) is like 3 rows with a single column each, a tall, thin structure with 3 elements.
When multiplied to create
Z, they are broadcasted into two dimensions to create a
Also, instead of using
gca() to create the 3D axes, I have used the
plt.axes() function with the
projection='3d' keyword argument, as suggested by the deprecation warning.
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