A brief summary for plotting in 3D with matplotlib. Let's plot the probability density function of the bivariate normal distribution in 3D.

See the official tutorial for details.

Import what you need for the time being. Also set the number of dimensions and parameters of the normal distribution.

```
import matplotlib
print(matplotlib.__version__)
# 1.5.1
import numpy as np
from scipy.stats import multivariate_normal
#for plotting
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
m = 2 #dimension
mean = np.zeros(m)
sigma = np.eye(m)
```

There seems to be no major change in the latest version (ver 2.2.2 stable version). See here for details. The mplot3d Toolkit

Surface Plot Try Surface Plot (Surface plot in Japanese?). Note that the data passed to the plot_surface function is a two-dimensional array.

```
N = 1000
x1 = np.linspace(-5, 5, N)
x2 = np.linspace(-5, 5, N)
X1, X2 = np.meshgrid(x1, x2)
X = np.c_[np.ravel(X1), np.ravel(X2)]
Y_plot = multivariate_normal.pdf(x=X, mean=mean, cov=sigma)
Y_plot = Y_plot.reshape(X1.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(X1, X2, Y_plot, cmap='bwr', linewidth=0)
fig.colorbar(surf)
ax.set_title("Surface Plot")
fig.show()
# X1.shape : (1000, 1000)
# X2.shape : (1000, 1000)
# Y_plot.shape : (1000, 1000)
```

Contour Plot Contour Plot can be done in the same way as surface plot.

```
N = 1000
x1 = np.linspace(-5, 5, N)
x2 = np.linspace(-5, 5, N)
X1, X2 = np.meshgrid(x1, x2)
X = np.c_[np.ravel(X1), np.ravel(X2)]
Y_plot = multivariate_normal.pdf(x=X, mean=mean, cov=sigma)
Y_plot = Y_plot.reshape(X1.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.contour(X1, X2, Y_plot)
ax.set_title("Contour Plot")
fig.show()
# X1.shape : (1000, 1000)
# X2.shape : (1000, 1000)
# Y_plot.shape : (1000, 1000)
```

Scatter Plot Unlike before, the data passed to the scatter plot is a one-dimensional array.

```
N = 100
x1 = np.linspace(-5, 5, N)
x2 = np.linspace(-5, 5, N)
X1, X2 = np.meshgrid(x1, x2)
X_plot = np.c_[np.ravel(X1), np.ravel(X2)]
y = multivariate_normal.pdf(X_plot, mean=mean, cov=sigma)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter3D(np.ravel(X1), np.ravel(X2), y)
ax.set_title("Scatter Plot")
plt.show()
# np.ravel(X1).shape : (10000,)
# np.ravel(X2).shape : (10000,)
# y.shape : (10000,)
```

Scatter plot is not for when you want to see the shape of such a function, so it can't be helped that it is hard to see.

Looking at other articles, there are examples of creating ax objects for 3D as follows, but

```
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
#<class 'mpl_toolkits.mplot3d.axes3d.Axes3D'>
```

In recent versions, it seems recommended to use this as per the tutorial.

```
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#<class 'matplotlib.axes._subplots.Axes3DSubplot'>
```

By the way, Axes3D is not explicitly used, but if you do not import it, you will get `KeyError: '3d'`

.

Also, it seems that you can create an ax object of the same class as follows.

```
fig = plt.figure()
ax = fig.gca(projection='3d')
#<class 'matplotlib.axes._subplots.Axes3DSubplot'>
```

There seems to be plotly that plots 3D nicely with python, so I'd like to find out soon.

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