diff --git a/galleries/examples/statistics/bxp.py b/galleries/examples/statistics/bxp.py index d78747523325..b12bfebd16cc 100644 --- a/galleries/examples/statistics/bxp.py +++ b/galleries/examples/statistics/bxp.py @@ -1,107 +1,65 @@ """ -======================= -Boxplot drawer function -======================= - -This example demonstrates how to pass pre-computed box plot -statistics to the box plot drawer. 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. - -A good general reference on boxplots and their history can be found -here: http://vita.had.co.nz/papers/boxplots.pdf -""" - -import matplotlib.pyplot as plt -import numpy as np - -import matplotlib.cbook as cbook +============================================= +Separate calculation and plotting of boxplots +============================================= -# fake data -np.random.seed(19680801) -data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75) -labels = list('ABCD') - -# compute the boxplot stats -stats = cbook.boxplot_stats(data, labels=labels, bootstrap=10000) +Drawing a `~.axes.Axes.boxplot` for a given data set, consists of two main operations, +that can also be used separately: -# %% -# After we've computed the stats, we can go through and change anything. -# Just to prove it, I'll set the median of each set to the median of all -# the data, and double the means +1. Calculating the boxplot statistics: `matplotlib.cbook.boxplot_stats` +2. Drawing the boxplot: `matplotlib.axes.Axes.bxp` -for n in range(len(stats)): - stats[n]['med'] = np.median(data) - stats[n]['mean'] *= 2 +Thus, ``ax.boxplot(data)`` is equivalent to :: -print(list(stats[0])) + stats = cbook.boxplot_stats(data) + ax.bxp(stats) -fs = 10 # fontsize - -# %% -# Demonstrate how to toggle the display of different elements: +All styling keyword arguments are identical between `~.axes.Axes.boxplot` and +`~.axes.Axes.bxp`, and they are passed through from `~.axes.Axes.boxplot` to +`~.axes.Axes.bxp`. However, the *tick_labels* parameter of `~.axes.Axes.boxplot` +translates to a generic *labels* parameter in `.boxplot_stats`, because the labels are +data-related and attached to the returned per-dataset dictionaries. -fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True) -axs[0, 0].bxp(stats) -axs[0, 0].set_title('Default', fontsize=fs) +The following code demonstrates the equivalence between the two methods. -axs[0, 1].bxp(stats, showmeans=True) -axs[0, 1].set_title('showmeans=True', fontsize=fs) +""" +# sphinx_gallery_thumbnail_number = 2 -axs[0, 2].bxp(stats, showmeans=True, meanline=True) -axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs) +import matplotlib.pyplot as plt +import numpy as np -axs[1, 0].bxp(stats, showbox=False, showcaps=False) -tufte_title = 'Tufte Style\n(showbox=False,\nshowcaps=False)' -axs[1, 0].set_title(tufte_title, fontsize=fs) +from matplotlib import cbook -axs[1, 1].bxp(stats, shownotches=True) -axs[1, 1].set_title('notch=True', fontsize=fs) +np.random.seed(19680801) +data = np.random.randn(20, 3) -axs[1, 2].bxp(stats, showfliers=False) -axs[1, 2].set_title('showfliers=False', fontsize=fs) +fig, (ax1, ax2) = plt.subplots(1, 2) -for ax in axs.flat: - ax.set_yscale('log') - ax.set_yticklabels([]) +# single boxplot call +ax1.boxplot(data, tick_labels=['A', 'B', 'C'], + patch_artist=True, boxprops={'facecolor': 'bisque'}) -fig.subplots_adjust(hspace=0.4) -plt.show() +# separate calculation of statistics and plotting +stats = cbook.boxplot_stats(data, labels=['A', 'B', 'C']) +ax2.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'}) # %% -# 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, axs = plt.subplots(nrows=2, ncols=2, figsize=(6, 6), sharey=True) -axs[0, 0].bxp(stats, boxprops=boxprops) -axs[0, 0].set_title('Custom boxprops', fontsize=fs) - -axs[0, 1].bxp(stats, flierprops=flierprops, medianprops=medianprops) -axs[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs) +# Using the separate functions allows to pre-calculate statistics, in case you need +# them explicitly for other purposes, or to reuse the statistics for multiple plots. +# +# Conversely, you can also use the `~.axes.Axes.bxp` function directly, if you already +# have the statistical parameters: -axs[1, 0].bxp(stats, meanprops=meanpointprops, meanline=False, - showmeans=True) -axs[1, 0].set_title('Custom mean\nas point', fontsize=fs) +fig, ax = plt.subplots() -axs[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True, - showmeans=True) -axs[1, 1].set_title('Custom mean\nas line', fontsize=fs) +stats = [ + dict(med=0, q1=-1, q3=1, whislo=-2, whishi=2, fliers=[-4, -3, 3, 4], label='A'), + dict(med=0, q1=-2, q3=2, whislo=-3, whishi=3, fliers=[], label='B'), + dict(med=0, q1=-3, q3=3, whislo=-4, whishi=4, fliers=[], label='C'), +] -for ax in axs.flat: - ax.set_yscale('log') - ax.set_yticklabels([]) +ax.bxp(stats, patch_artist=True, boxprops={'facecolor': 'bisque'}) -fig.suptitle("I never said they'd be pretty") -fig.subplots_adjust(hspace=0.4) plt.show() # %% @@ -112,4 +70,5 @@ # in this example: # # - `matplotlib.axes.Axes.bxp` +# - `matplotlib.axes.Axes.boxplot` # - `matplotlib.cbook.boxplot_stats` pFad - Phonifier reborn

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