Data Visualization with Matplotlib

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Getting Stared with Matplotlib - Create 3D Projections using Matplotlib

Creating 3D projections in Matplotlib is very simple. You need to import Axes3D, which registers the "3d" projection. Then create a subplot setting the projection to "3d". This returns an Axes3DSubplot object, which you can use to call plot_surface, giving x, y, and z coordinates, plus optional attributes.

  • Import Axes3D:

    from mpl_toolkits.mplot3d import << your code goes here >>
  • Import other libraries

    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib
  • Create the projection:

    x = np.linspace(-5, 5, 50)
    y = np.linspace(-5, 5, 50)
    X, Y = np.meshgrid(x, y)
    R = np.sqrt(X**2 + Y**2)
    Z = np.sin(R)

    Here, linspace returns evenly spaced numbers over a specified interval. meshgrid returns coordinate matrices from coordinate vectors.

  • Finally, we will create the figure, the subplot, and call the plot_surface with the coordinates we created earlier:

    figure = plt.figure(1, figsize = (12, 4))
    subplot3d = plt.subplot(111, projection='3d')
    surface = subplot3d.plot_surface(X, Y, Z, rstride=1, cstride=1,, linewidth=0.1)

    The figure module provides the top-level Artist , the Figure , which contains all the plot elements. The figsize marks the width and height of the plot.

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