

.. _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: http://scikit-learn.org/stable/modules/density.html




.. image:: /gallery/statistics/images/sphx_glr_boxplot_vs_violin_001.png
    :align: center





.. code-block:: python


    import matplotlib.pyplot as plt
    import numpy as np

    fig, axes = 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
    axes[0].violinplot(all_data,
                       showmeans=False,
                       showmedians=True)
    axes[0].set_title('Violin plot')

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

    # adding horizontal grid lines
    for ax in axes:
        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(axes, xticks=[y + 1 for y in range(len(all_data))],
             xticklabels=['x1', 'x2', 'x3', 'x4'])
    plt.show()

**Total running time of the script:** ( 0 minutes  0.094 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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