

.. _sphx_glr_gallery_statistics_customized_violin.py:


=========================
Violin plot customization
=========================

This example demonstrates how to fully customize violin plots.
The first plot shows the default style by providing only
the data. The second plot first limits what matplotlib draws
with additional kwargs. Then a simplified representation of
a box plot is drawn on top. Lastly, the styles of the artists
of the violins are modified.

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_customized_violin_001.png
    :align: center





.. code-block:: python


    import matplotlib.pyplot as plt
    import numpy as np


    def adjacent_values(vals, q1, q3):
        upper_adjacent_value = q3 + (q3 - q1) * 1.5
        upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1])

        lower_adjacent_value = q1 - (q3 - q1) * 1.5
        lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1)
        return lower_adjacent_value, upper_adjacent_value


    def set_axis_style(ax, labels):
        ax.get_xaxis().set_tick_params(direction='out')
        ax.xaxis.set_ticks_position('bottom')
        ax.set_xticks(np.arange(1, len(labels) + 1))
        ax.set_xticklabels(labels)
        ax.set_xlim(0.25, len(labels) + 0.75)
        ax.set_xlabel('Sample name')


    # create test data
    np.random.seed(19680801)
    data = [sorted(np.random.normal(0, std, 100)) for std in range(1, 5)]

    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(9, 4), sharey=True)

    ax1.set_title('Default violin plot')
    ax1.set_ylabel('Observed values')
    ax1.violinplot(data)

    ax2.set_title('Customized violin plot')
    parts = ax2.violinplot(
            data, showmeans=False, showmedians=False,
            showextrema=False)

    for pc in parts['bodies']:
        pc.set_facecolor('#D43F3A')
        pc.set_edgecolor('black')
        pc.set_alpha(1)

    quartile1, medians, quartile3 = np.percentile(data, [25, 50, 75], axis=1)
    whiskers = np.array([
        adjacent_values(sorted_array, q1, q3)
        for sorted_array, q1, q3 in zip(data, quartile1, quartile3)])
    whiskersMin, whiskersMax = whiskers[:, 0], whiskers[:, 1]

    inds = np.arange(1, len(medians) + 1)
    ax2.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)
    ax2.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)
    ax2.vlines(inds, whiskersMin, whiskersMax, color='k', linestyle='-', lw=1)

    # set style for the axes
    labels = ['A', 'B', 'C', 'D']
    for ax in [ax1, ax2]:
        set_axis_style(ax, labels)

    plt.subplots_adjust(bottom=0.15, wspace=0.05)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.086 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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