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Matplotlib.axes.Axes.boxplot() in Python

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  • Last Updated : 13 Apr, 2020
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Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. 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.boxplot() Function

The Axes.boxplot() 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.boxplot(self, x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None, *, data=None)

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

  • x: This parameter is a sequence of data.
  • notch: This parameter will produce a notched box plot if true. Otherwise, a rectangular boxplot is produced.
  • sym : This parameter is an optional parameter and contain string value. It is a default symbol for flier points.
  • vert: This parameter is an optional parameter and contain boolean value. It makes the boxes vertical if true.Otherwise horizontal.
  • whis : This parameter determines the reach of the whiskers to the beyond the first and third quartiles.
  • bootstrap : This parameter is also an optional parameter which contain boolean value and specifies whether to bootstrap the confidence intervals around the median for notched boxplots.
  • usermedians : This parameter is an array or sequence whose first dimension is compatible with x.
  • conf_intervals : This parameter is also an array or sequence whose first dimension is compatible with x and whose second dimension is 2
  • positions : This parameter is used to sets the positions of the boxes.
  • widths: This parameter is used to sets the width of each box 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.
  • labels : This parameter is the labels for each dataset.
  • 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.

Returns: This returns the following:

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

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

Example-1:




# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
   
np.random.seed(10**7)
  
val1 = np.random.rand(50) * 80
val2 = np.ones(80) * 50
val3 = np.random.rand(50) * 80 + 100
val4 = np.random.rand(50) * -80
data = np.concatenate((val1, val2, val3, val4))
  
fig1, ax1 = plt.subplots()
ax1.boxplot(data)
   
ax1.set_title('matplotlib.axes.Axes.boxplot() Example')
plt.show()

Output:

Example-2:




# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
   
np.random.seed(10**7)
  
val1 = np.random.rand(50) * 80
val2 = np.ones(25) * 80
val3 = np.random.rand(25) * 80 + 100
val4 = np.random.rand(25) * -80
data = np.concatenate((val1, val2, val3, val4))
data1 = np.concatenate((val2, val4, val1, val3))
data = [data, data1]
  
fig1, ax1 = plt.subplots()
ax1.boxplot(data, notch = True, vert = False, whis = 0.75)
   
ax1.set_title('matplotlib.axes.Axes.boxplot() Example')
plt.show()

Output:


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