.. 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:: default

    """
    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.
    """

    import matplotlib.pyplot as plt
    import numpy as np

    x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]
    y1 = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]
    y2 = [9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74]
    y3 = [7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73]
    x4 = [8, 8, 8, 8, 8, 8, 8, 19, 8, 8, 8]
    y4 = [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


    fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)
    axs[0, 0].set(xlim=(0, 20), ylim=(2, 14))
    axs[0, 0].set(xticks=(0, 10, 20), yticks=(4, 8, 12))

    xfit = np.array([np.min(x), np.max(x)])
    axs[0, 0].plot(x, y1, 'ks', xfit, fit(xfit), 'r-', lw=2)
    axs[0, 1].plot(x, y2, 'ks', xfit, fit(xfit), 'r-', lw=2)
    axs[1, 0].plot(x, y3, 'ks', xfit, fit(xfit), 'r-', lw=2)
    xfit = np.array([np.min(x4), np.max(x4)])
    axs[1, 1].plot(x4, y4, 'ks', xfit, fit(xfit), 'r-', lw=2)

    for ax, label in zip(axs.flat, ['I', 'II', 'III', 'IV']):
        ax.label_outer()
        ax.text(3, 12, label, 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>`_