.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/specialty_plots/anscombe.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. 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
==================

`Anscombe's quartet`_ is a group of datasets (x, y) that have the same mean,
standard deviation, and regression line, but which are qualitatively different.

It is often used to illustrate the importance of looking at a set of data
graphically and not only relying on basic statistic properties.

.. _Anscombe's quartet: https://en.wikipedia.org/wiki/Anscombe%27s_quartet

.. GENERATED FROM PYTHON SOURCE LINES 14-57



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





.. code-block:: default


    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]

    datasets = {
        'I': (x, y1),
        'II': (x, y2),
        'III': (x, y3),
        'IV': (x4, y4)
    }

    fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(6, 6),
                            gridspec_kw={'wspace': 0.08, 'hspace': 0.08})
    axs[0, 0].set(xlim=(0, 20), ylim=(2, 14))
    axs[0, 0].set(xticks=(0, 10, 20), yticks=(4, 8, 12))

    for ax, (label, (x, y)) in zip(axs.flat, datasets.items()):
        ax.text(0.1, 0.9, label, fontsize=20, transform=ax.transAxes, va='top')
        ax.tick_params(direction='in', top=True, right=True)
        ax.plot(x, y, 'o')

        # linear regression
        p1, p0 = np.polyfit(x, y, deg=1)
        x_lin = np.array([np.min(x), np.max(x)])
        y_lin = p1 * x_lin + p0
        ax.plot(x_lin, y_lin, 'r-', lw=2)

        # add text box for the statistics
        stats = (f'$\\mu$ = {np.mean(y):.2f}\n'
                 f'$\\sigma$ = {np.std(y):.2f}\n'
                 f'$r$ = {np.corrcoef(x, y)[0][1]:.2f}')
        bbox = dict(boxstyle='round', fc='blanchedalmond', ec='orange', alpha=0.5)
        ax.text(0.95, 0.07, stats, fontsize=9, bbox=bbox,
                transform=ax.transAxes, horizontalalignment='right')

    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 sphx-glr-download-python

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



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :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>`_