.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_gallery_specialty_plots_anscombe.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_specialty_plots_anscombe.py:


==================
Anscombe's Quartet
==================





.. image:: /gallery/specialty_plots/images/sphx_glr_anscombe_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    mean=7.50, std=1.94, r=0.82
    mean=7.50, std=1.94, r=0.82
    mean=7.50, std=1.94, r=0.82
    mean=7.50, std=1.94, r=0.82




|


.. code-block:: python

    """
    Edward Tufte uses this example from Anscombe to show 4 datasets of x
    and y that have the same mean, standard deviation, and regression
    line, but which are qualitatively different.

    matplotlib fun for a rainy day
    """

    import matplotlib.pyplot as plt
    import numpy as np

    x = np.array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
    y1 = np.array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
    y2 = np.array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
    y3 = np.array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
    x4 = np.array([8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8])
    y4 = np.array([6.58, 5.76, 7.71, 8.84, 8.47, 7.04, 5.25, 12.50, 5.56, 7.91, 6.89])


    def fit(x):
        return 3 + 0.5 * x


    xfit = np.array([np.min(x), np.max(x)])

    plt.subplot(221)
    plt.plot(x, y1, 'ks', xfit, fit(xfit), 'r-', lw=2)
    plt.axis([2, 20, 2, 14])
    plt.setp(plt.gca(), xticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
    plt.text(3, 12, 'I', fontsize=20)

    plt.subplot(222)
    plt.plot(x, y2, 'ks', xfit, fit(xfit), 'r-', lw=2)
    plt.axis([2, 20, 2, 14])
    plt.setp(plt.gca(), xticks=(0, 10, 20), xticklabels=[],
             yticks=(4, 8, 12), yticklabels=[], )
    plt.text(3, 12, 'II', fontsize=20)

    plt.subplot(223)
    plt.plot(x, y3, 'ks', xfit, fit(xfit), 'r-', lw=2)
    plt.axis([2, 20, 2, 14])
    plt.text(3, 12, 'III', fontsize=20)
    plt.setp(plt.gca(), yticks=(4, 8, 12), xticks=(0, 10, 20))

    plt.subplot(224)
    xfit = np.array([np.min(x4), np.max(x4)])
    plt.plot(x4, y4, 'ks', xfit, fit(xfit), 'r-', lw=2)
    plt.axis([2, 20, 2, 14])
    plt.setp(plt.gca(), yticklabels=[], yticks=(4, 8, 12), xticks=(0, 10, 20))
    plt.text(3, 12, 'IV', fontsize=20)

    # verify the stats
    pairs = (x, y1), (x, y2), (x, y3), (x4, y4)
    for x, y in pairs:
        print('mean=%1.2f, std=%1.2f, r=%1.2f' % (np.mean(y), np.std(y),
              np.corrcoef(x, y)[0][1]))

    plt.show()


.. _sphx_glr_download_gallery_specialty_plots_anscombe.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

    Keywords: matplotlib code example, codex, python plot, pyplot
    `Gallery generated by Sphinx-Gallery
    <https://sphinx-gallery.readthedocs.io>`_