.. _sphx_glr_gallery_mplot3d_hist3d.py:


==============================
Create 3D histogram of 2D data
==============================

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




.. image:: /gallery/mplot3d/images/sphx_glr_hist3d_001.png
    :align: center





.. code-block:: python


    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  0.023 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: hist3d.py <hist3d.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: hist3d.ipynb <hist3d.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_