Creating three-dimensional plots

Matplotlib offers several different ways to visualize three-dimensional data. In this recipe, we will demonstrate the following methods:

• Drawing surfaces plots
• Drawing two-dimensional contour plots
• Using color maps and color bars

Getting ready

Start Jupyter and run the following three commands in an execution cell:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

How to do it…

Run the following code in a Jupyter code cell:

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
f = lambda x,y: x**3 - 3*x*y**2
fig = plt.figure(figsize=(12,6))
ax = fig.add_subplot(1,2,1,projection='3d')
xvalues = np.linspace(-2,2,100)
yvalues = np.linspace(-2,2,100)
xgrid, ygrid = np.meshgrid(xvalues, yvalues)
zvalues = f(xgrid, ygrid)
surf = ax.plot_surface(xgrid, ygrid, zvalues,
                       rstride=5, cstride=5,
                       linewidth=0, cmap=cm.plasma)
ax = fig.add_subplot(1,2,2)
plt.contourf(xgrid, ygrid, zvalues, 30, 
      ...