.. _statistics-boxplot_vs_violin_demo:

statistics example code: boxplot_vs_violin_demo.py
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.. plot:: /home/tcaswell/source/my_source/matplotlib/doc/mpl_examples/statistics/boxplot_vs_violin_demo.py

::

    # Box plot - 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 x the inter-quartile range as outliers
    # above or below the whiskers wheras violin plots show the whole range of the data.
    #
    # Violin plots require matplotlib >= 1.4.
    
    import matplotlib.pyplot as plt
    import numpy as np
    
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
    
    # 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('xlabel')
        ax.set_ylabel('ylabel')
    
    # add x-tick labels
    plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
             xticklabels=['x1', 'x2', 'x3', 'x4'])
    plt.show()
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)