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Artist customization in box plots#
This example demonstrates how to use the various keyword arguments 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: https://vita.had.co.nz/papers/boxplots.pdf
import matplotlib.pyplot as plt
import numpy as np
# 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, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)
axs[0, 0].boxplot(data, labels=labels)
axs[0, 0].set_title('Default', fontsize=fs)
axs[0, 1].boxplot(data, labels=labels, showmeans=True)
axs[0, 1].set_title('showmeans=True', fontsize=fs)
axs[0, 2].boxplot(data, labels=labels, showmeans=True, meanline=True)
axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)
axs[1, 0].boxplot(data, labels=labels, showbox=False, showcaps=False)
tufte_title = 'Tufte Style \n(showbox=False,\nshowcaps=False)'
axs[1, 0].set_title(tufte_title, fontsize=fs)
axs[1, 1].boxplot(data, labels=labels, notch=True, bootstrap=10000)
axs[1, 1].set_title('notch=True,\nbootstrap=10000', fontsize=fs)
axs[1, 2].boxplot(data, labels=labels, showfliers=False)
axs[1, 2].set_title('showfliers=False', fontsize=fs)
for ax in axs.flat:
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,
markeredgecolor='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=3, figsize=(6, 6), sharey=True)
axs[0, 0].boxplot(data, boxprops=boxprops)
axs[0, 0].set_title('Custom boxprops', fontsize=fs)
axs[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)
axs[0, 1].set_title('Custom medianprops\nand flierprops', fontsize=fs)
axs[0, 2].boxplot(data, whis=(0, 100))
axs[0, 2].set_title('whis=(0, 100)', fontsize=fs)
axs[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,
showmeans=True)
axs[1, 0].set_title('Custom mean\nas point', fontsize=fs)
axs[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,
showmeans=True)
axs[1, 1].set_title('Custom mean\nas line', fontsize=fs)
axs[1, 2].boxplot(data, whis=[15, 85])
axs[1, 2].set_title('whis=[15, 85]\n#percentiles', fontsize=fs)
for ax in axs.flat:
ax.set_yscale('log')
ax.set_yticklabels([])
fig.suptitle("I never said they'd be pretty")
fig.subplots_adjust(hspace=0.4)
plt.show()
References
The use of the following functions, methods, classes and modules is shown in this example:
Total running time of the script: (0 minutes 1.144 seconds)