# (Fixed) TypeError: FigureBase.gca() got an unexpected keyword argument ‘projection’ : Chris

**(Fixed) TypeError: FigureBase.gca() got an unexpected keyword argument ‘projection’**

**by: Chris**

*blow post content copied from Be on the Right Side of Change*

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When trying to plot a 3D normal distribution recently, I encountered the following error:

```
TypeError Traceback (most recent call last)
<ipython-input-10-ee1b0cd4b744> in <cell line: 32>()
30 fig = plt.figure()
31
---> 32 ax = fig.gca(projection='3d')
33
34 # create a 3D surface plot of the multivariate normal distribution
👉
```**TypeError: FigureBase.gca() got an unexpected keyword argument 'projection'** 🤔
<Figure size 640x480 with 0 Axes>

This happened after running the following code snippet — see the highlighted line:

ax = fig.gca(projection='3d')

Here’s the full context in case you want to reproduce the error:

# import necessary libraries import numpy as np import matplotlib.pyplot as plt from scipy.stats import multivariate_normal from mpl_toolkits.mplot3d import Axes3D # define parameters for x and y distributions mu_x = 0 # mean of x variance_x = 3 # variance of x mu_y = 0 # mean of y variance_y = 15 # variance of y # define a grid for x and y values x = np.linspace(-10, 10, 500) # generate 500 points between -10 and 10 for x y = np.linspace(-10, 10, 500) # generate 500 points between -10 and 10 for y X, Y = np.meshgrid(x, y) # create a grid for (x,y) pairs # create an empty array of the same shape as X to hold the (x, y) coordinates pos = np.empty(X.shape + (2,)) # fill the pos array with the x and y coordinates pos[:, :, 0] = X pos[:, :, 1] = Y # create a multivariate normal distribution using the defined parameters rv = multivariate_normal([mu_x, mu_y], [[variance_x, 0], [0, variance_y]]) # create a new figure for 3D plot fig = plt.figure() ax = fig.gca(projection='3d') # create a 3D surface plot of the multivariate normal distribution ax.plot_surface(X, Y, rv.pdf(pos), cmap='viridis', linewidth=0) # set labels for the axes ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') # display the 3D plot plt.show()

## Solution

The simple solution is to replace the line:

ax = fig.gca(projection='3d')

with the line

ax = fig.add_subplot(projection='3d')

And voila, it’ll work!

This error appears because, in Matplotlib 3.4 and later versions, the function `gca()`

no longer takes keyword arguments. However, many code snippets on the web still have the same old and stale recommendations.

Here’s the updated code snippet with the line swapped, i.e., I replaced `fig.gca(projection='3d')`

with `fig.add_subplot(projection='3d')`

.

# import necessary libraries import numpy as np import matplotlib.pyplot as plt from scipy.stats import multivariate_normal from mpl_toolkits.mplot3d import Axes3D # define parameters for x and y distributions mu_x = 0 # mean of x variance_x = 3 # variance of x mu_y = 0 # mean of y variance_y = 15 # variance of y # define a grid for x and y values x = np.linspace(-10, 10, 500) # generate 500 points between -10 and 10 for x y = np.linspace(-10, 10, 500) # generate 500 points between -10 and 10 for y X, Y = np.meshgrid(x, y) # create a grid for (x,y) pairs # create an empty array of the same shape as X to hold the (x, y) coordinates pos = np.empty(X.shape + (2,)) # fill the pos array with the x and y coordinates pos[:, :, 0] = X pos[:, :, 1] = Y # create a multivariate normal distribution using the defined parameters rv = multivariate_normal([mu_x, mu_y], [[variance_x, 0], [0, variance_y]]) # create a new figure for 3D plot fig = plt.figure() # add a 3D subplot to the figure ax = fig.add_subplot(projection='3d') # create a 3D surface plot of the multivariate normal distribution ax.plot_surface(X, Y, rv.pdf(pos), cmap='viridis', linewidth=0) # set labels for the axes ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') # display the 3D plot plt.show()

The beautiful result when it works

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July 23, 2023 at 12:31AM

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