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# Violin plot basics

Violin plots are similar to histograms and box plots in that they show an abstract representation of the probability distribution of the sample. Rather than showing counts of data points that fall into bins or order statistics, violin plots use kernel density estimation (KDE) to compute an empirical distribution of the sample. That computation is controlled by several parameters. This example demonstrates how to modify the number of points at which the KDE is evaluated (`points`) and how to modify the band-width of the KDE (`bw_method`).

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

    import random
    import numpy as np
    import matplotlib.pyplot as plt

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

    # fake data
    fs = 10  # fontsize
    pos = [1, 2, 4, 5, 7, 8]
    data = [np.random.normal(0, std, size=100) for std in pos]

    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6))

    axes[0, 0].violinplot(data, pos, points=20, widths=0.3,
                          showmeans=True, showextrema=True, showmedians=True)
    axes[0, 0].set_title('Custom violinplot 1', fontsize=fs)

    axes[0, 1].violinplot(data, pos, points=40, widths=0.5,
                          showmeans=True, showextrema=True, showmedians=True,
                          bw_method='silverman')
    axes[0, 1].set_title('Custom violinplot 2', fontsize=fs)

    axes[0, 2].violinplot(data, pos, points=60, widths=0.7, showmeans=True,
                          showextrema=True, showmedians=True, bw_method=0.5)
    axes[0, 2].set_title('Custom violinplot 3', fontsize=fs)

    axes[1, 0].violinplot(data, pos, points=80, vert=False, widths=0.7,
                          showmeans=True, showextrema=True, showmedians=True)
    axes[1, 0].set_title('Custom violinplot 4', fontsize=fs)

    axes[1, 1].violinplot(data, pos, points=100, vert=False, widths=0.9,
                          showmeans=True, showextrema=True, showmedians=True,
                          bw_method='silverman')
    axes[1, 1].set_title('Custom violinplot 5', fontsize=fs)

    axes[1, 2].violinplot(data, pos, points=200, vert=False, widths=1.1,
                          showmeans=True, showextrema=True, showmedians=True,
                          bw_method=0.5)
    axes[1, 2].set_title('Custom violinplot 6', fontsize=fs)

    for ax in axes.flatten():
        ax.set_yticklabels([])

    fig.suptitle("Violin Plotting Examples")
    fig.subplots_adjust(hspace=0.4)
    plt.show()

Total running time of the script: ( 0 minutes 0.292 seconds)

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