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# Create 3D histogram of 2D data

Demo of a histogram for 2 dimensional data as a bar graph in 3D.

    from mpl_toolkits.mplot3d import Axes3D
    import matplotlib.pyplot as plt
    import numpy as np

    # Fixing random state for reproducibility
    np.random.seed(19680801)

    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    x, y = np.random.rand(2, 100) * 4
    hist, xedges, yedges = np.histogram2d(x, y, bins=4, range=[[0, 4], [0, 4]])

    # Construct arrays for the anchor positions of the 16 bars.
    # Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten xpos,
    # ypos in column-major order. For numpy >= 1.7, we could instead call meshgrid
    # with indexing='ij'.
    xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
    xpos = xpos.flatten('F')
    ypos = ypos.flatten('F')
    zpos = np.zeros_like(xpos)

    # Construct arrays with the dimensions for the 16 bars.
    dx = 0.5 * np.ones_like(zpos)
    dy = dx.copy()
    dz = hist.flatten()

    ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')

    plt.show()

Total running time of the script: ( 0 minutes 0.050 seconds)

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