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Merge pull request #27965 from timhoffm/bxp-example
DOC: Rewrite the example illustrating bxp()
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‎galleries/examples/statistics/bxp.py

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"""
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=======================
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Boxplot drawer function
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=======================
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This example demonstrates how to pass pre-computed box plot
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statistics to the box plot drawer. The first figure demonstrates
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how to remove and add individual components (note that the
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mean is the only value not shown by default). The second
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figure demonstrates how the styles of the artists can
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be customized.
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A good general reference on boxplots and their history can be found
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here: http://vita.had.co.nz/papers/boxplots.pdf
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import matplotlib.cbook as cbook
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=============================================
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Separate calculation and plotting of boxplots
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=============================================
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# fake data
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np.random.seed(19680801)
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data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
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labels = list('ABCD')
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# compute the boxplot stats
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stats = cbook.boxplot_stats(data, labels=labels, bootstrap=10000)
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Drawing a `~.axes.Axes.boxplot` for a given data set, consists of two main operations,
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that can also be used separately:
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# %%
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# After we've computed the stats, we can go through and change anything.
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# Just to prove it, I'll set the median of each set to the median of all
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# the data, and double the means
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1. Calculating the boxplot statistics: `matplotlib.cbook.boxplot_stats`
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2. Drawing the boxplot: `matplotlib.axes.Axes.bxp`
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for n in range(len(stats)):
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stats[n]['med'] = np.median(data)
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stats[n]['mean'] *= 2
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Thus, ``ax.boxplot(data)`` is equivalent to ::
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print(list(stats[0]))
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stats = cbook.boxplot_stats(data)
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ax.bxp(stats)
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fs = 10 # fontsize
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# %%
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# Demonstrate how to toggle the display of different elements:
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All styling keyword arguments are identical between `~.axes.Axes.boxplot` and
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`~.axes.Axes.bxp`, and they are passed through from `~.axes.Axes.boxplot` to
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`~.axes.Axes.bxp`. However, the *tick_labels* parameter of `~.axes.Axes.boxplot`
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translates to a generic *labels* parameter in `.boxplot_stats`, because the labels are
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data-related and attached to the returned per-dataset dictionaries.
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fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
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axs[0, 0].bxp(stats)
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axs[0, 0].set_title('Default', fontsize=fs)
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The following code demonstrates the equivalence between the two methods.
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axs[0, 1].bxp(stats, showmeans=True)
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axs[0, 1].set_title('showmeans=True', fontsize=fs)
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"""
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# sphinx_gallery_thumbnail_number = 2
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axs[0, 2].bxp(stats, showmeans=True, meanline=True)
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axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
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import matplotlib.pyplot as plt
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import numpy as np
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axs[1, 0].bxp(stats, showbox=False, showcaps=False)
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tufte_title = 'Tufte Style\n(showbox=False,\nshowcaps=False)'
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axs[1, 0].set_title(tufte_title, fontsize=fs)
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from matplotlib import cbook
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axs[1, 1].bxp(stats, shownotches=True)
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axs[1, 1].set_title('notch=True', fontsize=fs)
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np.random.seed(19680801)
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data = np.random.randn(20, 3)
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axs[1, 2].bxp(stats, showfliers=False)
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axs[1, 2].set_title('showfliers=False', fontsize=fs)
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fig, (ax1, ax2) = plt.subplots(1, 2)
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for ax in axs.flat:
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ax.set_yscale('log')
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ax.set_yticklabels([])
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# single boxplot call
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ax1.boxplot(data, tick_labels=['A', 'B', 'C'],
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patch_artist=True, boxprops={'facecolor': 'bisque'})
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fig.subplots_adjust(hspace=0.4)
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plt.show()
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# separate calculation of statistics and plotting
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stats = cbook.boxplot_stats(data, labels=['A', 'B', 'C'])
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ax2.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'})
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# %%
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# Demonstrate how to customize the display different elements:
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boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')
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flierprops = dict(marker='o', markerfacecolor='green', markersize=12,
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linestyle='none')
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medianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')
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meanpointprops = dict(marker='D', markeredgecolor='black',
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markerfacecolor='firebrick')
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meanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')
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fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(6, 6), sharey=True)
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axs[0, 0].bxp(stats, boxprops=boxprops)
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axs[0, 0].set_title('Custom boxprops', fontsize=fs)
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axs[0, 1].bxp(stats, flierprops=flierprops, medianprops=medianprops)
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axs[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)
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# Using the separate functions allows to pre-calculate statistics, in case you need
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# them explicitly for other purposes, or to reuse the statistics for multiple plots.
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#
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# Conversely, you can also use the `~.axes.Axes.bxp` function directly, if you already
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# have the statistical parameters:
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axs[1, 0].bxp(stats, meanprops=meanpointprops, meanline=False,
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showmeans=True)
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axs[1, 0].set_title('Custom mean\nas point', fontsize=fs)
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fig, ax = plt.subplots()
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axs[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True,
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showmeans=True)
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axs[1, 1].set_title('Custom mean\nas line', fontsize=fs)
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stats = [
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dict(med=0, q1=-1, q3=1, whislo=-2, whishi=2, fliers=[-4, -3, 3, 4], label='A'),
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dict(med=0, q1=-2, q3=2, whislo=-3, whishi=3, fliers=[], label='B'),
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dict(med=0, q1=-3, q3=3, whislo=-4, whishi=4, fliers=[], label='C'),
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]
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for ax in axs.flat:
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ax.set_yscale('log')
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ax.set_yticklabels([])
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ax.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'})
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fig.suptitle("I never said they'd be pretty")
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fig.subplots_adjust(hspace=0.4)
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plt.show()
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# %%
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# in this example:
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#
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# - `matplotlib.axes.Axes.bxp`
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# - `matplotlib.axes.Axes.boxplot`
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# - `matplotlib.cbook.boxplot_stats`

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