Matplotlib.axes.Axes.bxp() in Python

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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() 