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# Artist customization in box plots

This example demonstrates how to use the various kwargs to fully customize box plots. The first figure demonstrates how to remove and add individual components (note that the mean is the only value not shown by default). The second figure demonstrates how the styles of the artists can be customized. It also demonstrates how to set the limit of the whiskers to specific percentiles (lower right axes)

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

    import numpy as np
    import matplotlib.pyplot as plt

    # fake data
    np.random.seed(19680801)
    data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
    labels = list('ABCD')
    fs = 10  # fontsize

    # demonstrate how to toggle the display of different elements:
    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
    axes[0, 0].boxplot(data, labels=labels)
    axes[0, 0].set_title('Default', fontsize=fs)

    axes[0, 1].boxplot(data, labels=labels, showmeans=True)
    axes[0, 1].set_title('showmeans=True', fontsize=fs)

    axes[0, 2].boxplot(data, labels=labels, showmeans=True, meanline=True)
    axes[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)

    axes[1, 0].boxplot(data, labels=labels, showbox=False, showcaps=False)
    tufte_title = 'Tufte Style \n(showbox=False,\nshowcaps=False)'
    axes[1, 0].set_title(tufte_title, fontsize=fs)

    axes[1, 1].boxplot(data, labels=labels, notch=True, bootstrap=10000)
    axes[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs)

    axes[1, 2].boxplot(data, labels=labels, showfliers=False)
    axes[1, 2].set_title('showfliers=False', fontsize=fs)

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

    fig.subplots_adjust(hspace=0.4)
    plt.show()

    # demonstrate how to customize the display different elements:
    boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
    flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
                      linestyle='none')
    medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
    meanpointprops = dict(marker='D', markeredgecolor='black',
                          markerfacecolor='firebrick')
    meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')

    fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
    axes[0, 0].boxplot(data, boxprops=boxprops)
    axes[0, 0].set_title('Custom boxprops', fontsize=fs)

    axes[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)
    axes[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)

    axes[0, 2].boxplot(data, whis='range')
    axes[0, 2].set_title('whis="range"', fontsize=fs)

    axes[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,
                       showmeans=True)
    axes[1, 0].set_title('Custom mean\nas point', fontsize=fs)

    axes[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,
                       showmeans=True)
    axes[1, 1].set_title('Custom mean\nas line', fontsize=fs)

    axes[1, 2].boxplot(data, whis=[15, 85])
    axes[1, 2].set_title('whis=[15, 85]\n#percentiles', fontsize=fs)

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

    fig.suptitle("I never said they'd be pretty")
    fig.subplots_adjust(hspace=0.4)
    plt.show()

Total running time of the script: ( 0 minutes 0.879 seconds)

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