Matplotlib.axes.Axes.bxp() in Python
Last Updated :
13 Apr, 2020
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()
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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()
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Output:
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