Matplotlib.axes.Axes.bxp() in Python

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.

matplotlib.axes.Axes.bxp() Function

The Axes.bxp() function in axes module of matplotlib library is used to make a box and whisker plot for each column of x or each vector in sequence x.

Syntax: Axes.bxp(self, bxpstats, positions=None, widths=None, vert=True, patch_artist=False, shownotches=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, whiskerprops=None, flierprops=None, medianprops=None, capprops=None, meanprops=None, meanline=False, manage_ticks=True, zorder=None)

Parameters: This method accept the following parameters that are described below:

• bxpstats : This parameter is alist of dictionaries containing stats for each boxplot.
• positions : This parameter is used to sets the positions of the violins.
• vert: This parameter is an optional parameter and contain boolean value. It makes the vertical violin plot if true.Otherwise horizontal.
• widths: This parameter is used to sets the width of each violin either with a scalar or a sequence.
• patch_artist : This parameter is used to produce boxes with the Line2D artist if it is false. Otherwise, boxes with Patch artists.
• manage_ticks : This parameter is used to adjust the tick locations and labels.
• zorder : This parameter is used to sets the zorder of the boxplot.
• shownotches: This parameter contain boolean value. It is used to produce a notched and rectangular box plot.
• showmeans : This parameter contain boolean value. It is used to toggle rendering of the means.
• showcaps : This parameter contain boolean value. It is used to toggle rendering of the caps.
• showfliers : This parameter contain boolean value. It is used to toggle rendering of the fliers.
• boxprops : This parameter is used to set the plotting style of the boxes.
• whiskerprops : This parameter is used to set the plotting style of the whiskers.
• capprops : This parameter is used to set the plotting style of the caps.
• flierprops : This parameter is used to set the plotting style of the fliers.
• medianprops : This parameter is used to set the plotting style of the medians.
• meanprops : This parameter is used to set the plotting style of the means.

Returns: This returns the following:

• result :This returns the dictionary which maps each component of the violinplot to a list of the matplotlib.lines.Line2D instances.

Below examples illustrate the matplotlib.axes.Axes.bxp() function in matplotlib.axes:

Example-1:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

np.random.seed(10**7)
data = np.random.lognormal(size =(10, 4),
                           mean = 4.5,
                           sigma = 4.75)

labels = ['G1', 'G2', 'G3', 'G4']

result = cbook.boxplot_stats(data,
                             labels = labels,
                             bootstrap = 1000)

for n in range(len(result)):
    result[n]['med'] = np.median(data)
    result[n]['mean'] *= 0.1

fig, axes1 = plt.subplots()
axes1.bxp(result)

axes1.set_title('matplotlib.axes.Axes.bxp() Example')
plt.show()

Output:

Example-2:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook

np.random.seed(10**7)
data = np.random.lognormal(size =(37, 4),
                           mean = 4.5,
                           sigma = 1.75)
labels = ['G1', 'G2', 'G3', 'G4']

stats = cbook.boxplot_stats(data, labels = labels,
                            bootstrap = 100)

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

fig, [axes1, axes2, axes3] = plt.subplots(nrows = 1,
                                          ncols = 3,
                                          sharey = True)

axes1.bxp(stats)
axes2.bxp(stats, showmeans = True)
axes3.bxp(stats, showmeans = True, meanline = True)

axes2.set_title('matplotlib.axes.Axes.bxp() Example')
plt.show()

Output: