"""
=========================================
Demo of 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(937)
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() 

　

　

Traceback (most recent call last):
  File "M:\______\boxplot_demo.py", line 73, in 
    axes[0, 2].boxplot(data, whis='range')
  File "C:\Users\______\AppData\Local\Programs\Python\Python311\Lib
    \site-packages\matplotlib\__init__.py", line 1459, in inner
    return func(ax, *map(sanitize_sequence, args), **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\______\AppData\Local\Programs\Python\Python311\Lib
    \site-packages\matplotlib\axes\_axes.py", line 3914, in boxplot
    bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\______\AppData\Local\Programs\Python\Python311\Lib
    \site-packages\matplotlib\cbook\__init__.py", line 1254, in boxplot_stats
    raise ValueError('whis must be a float or list of percentiles')
ValueError: whis must be a float or list of percentiles 

Traceback (most recent call last):
  File "E:\______\boxplot_demo.py", line 73, in 
    axes[0, 2].boxplot(data, whis='range')
  File "C:\Program Files\Python312\Lib\site-packages\matplotlib\__init__.py", 
    line 1478, in inner
    return func(ax, *map(sanitize_sequence, args), **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Program Files\Python312\Lib\site-packages\matplotlib\axes\_axes.py", 
    line 3947, in boxplot
    bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Program Files\Python312\Lib\site-packages\matplotlib\cbook.py", 
    line 1264, in boxplot_stats
    raise ValueError('whis must be a float or list of percentiles')
ValueError: whis must be a float or list of percentiles 

"""
=======================
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

# 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)

# %%
# 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

for n in range(len(stats)):
    stats[n]['med'] = np.median(data)
    stats[n]['mean'] *= 2

print(list(stats[0]))

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].bxp(stats)
axs[0, 0].set_title('Default', fontsize=fs)

axs[0, 1].bxp(stats, showmeans=True)
axs[0, 1].set_title('showmeans=True', fontsize=fs)

axs[0, 2].bxp(stats, showmeans=True, meanline=True)
axs[0, 2].set_title('showmeans=True,\nmeanline=True', fontsize=fs)

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)

axs[1, 1].bxp(stats, shownotches=True)
axs[1, 1].set_title('notch=True', fontsize=fs)

axs[1, 2].bxp(stats, 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,
                  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)

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

axs[1, 1].bxp(stats, meanprops=meanlineprops, meanline=True,
              showmeans=True)
axs[1, 1].set_title('Custom mean\nas line', 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()

# %%
#
# .. admonition:: References
#
#    The use of the following functions, methods, classes and modules is shown
#    in this example:
#
#    - `matplotlib.axes.Axes.bxp`
#    - `matplotlib.cbook.boxplot_stats` 

　Python 3.11.6 (matplotlib 3.7.1) 及び Python 3.12.0 (matplotlib 3.8.1) 共に、正常実行です。

　

　

['label', 'mean', 'iqr', 'cilo', 'cihi', 'whishi', 'whislo', 'fliers', 'q1', 'med', 'q3'] 

• 2023/12/10 Ver=1.04 Python 3.12.0 (matplotlib 3.8.1)で確認
• 2023/12/10 Ver=1.04 Python 3.11.6 (matplotlib 3.7.1)で確認
• 2023/04/05 Ver=1.03 Python 3.11.2 で確認
• 2020/11/02 Ver=1.01 Python 3.7.8 で確認
• 2018/12/06 Ver=1.01 初版リリース

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