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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_statistics_boxplot_vs_violin.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_statistics_boxplot_vs_violin.py:


===================================
Box plot vs. violin plot comparison
===================================

Note that although violin plots are closely related to Tukey's (1977)
box plots, they add useful information such as the distribution of the
sample data (density trace).

By default, box plots show data points outside 1.5 * the inter-quartile
range as outliers above or below the whiskers whereas violin plots show
the whole range of the data.

A good general reference on boxplots and their history can be found
here: http://vita.had.co.nz/papers/boxplots.pdf

Violin plots require matplotlib >= 1.4.

For more information on violin plots, the scikit-learn docs have a great
section: https://scikit-learn.org/stable/modules/density.html

.. GENERATED FROM PYTHON SOURCE LINES 22-56



.. image:: /gallery/statistics/images/sphx_glr_boxplot_vs_violin_001.png
    :alt: Violin plot, Box plot
    :class: sphx-glr-single-img





.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))

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


    # generate some random test data
    all_data = [np.random.normal(0, std, 100) for std in range(6, 10)]

    # plot violin plot
    axs[0].violinplot(all_data,
                      showmeans=False,
                      showmedians=True)
    axs[0].set_title('Violin plot')

    # plot box plot
    axs[1].boxplot(all_data)
    axs[1].set_title('Box plot')

    # adding horizontal grid lines
    for ax in axs:
        ax.yaxis.grid(True)
        ax.set_xticks([y + 1 for y in range(len(all_data))])
        ax.set_xlabel('Four separate samples')
        ax.set_ylabel('Observed values')

    # add x-tick labels
    plt.setp(axs, xticks=[y + 1 for y in range(len(all_data))],
             xticklabels=['x1', 'x2', 'x3', 'x4'])
    plt.show()


.. _sphx_glr_download_gallery_statistics_boxplot_vs_violin.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: boxplot_vs_violin.py <boxplot_vs_violin.py>`



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

     :download:`Download Jupyter notebook: boxplot_vs_violin.ipynb <boxplot_vs_violin.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>`_
