Skip to content
Related Articles

Related Articles

matplotlib.axes.Axes.pie() in Python
  • Last Updated : 12 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.pie() Function

The Axes.pie() function in axes module of matplotlib library is used to plot a pie chart.

Syntax: Axes.pie(self, x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, *, data=None)

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

  • x: This parameter is the wedge sizes.
  • explode: This parameter is the is a len(x) array which specifies the fraction of the radius with which to offset each wedge.
  • autopct: This parameter is a string or function used to label the wedges with their numeric value.
  • colors: This parameter is the sequence of matplotlib color args through which the pie chart will cycle.
  • label: This parameter is the sequence of strings providing the labels for each wedge.
  • pctdistance: This parameter is the ratio between the center of each pie slice and the start of the text generated by autopct.
  • shadow: This parameter is the used to draw a shadow beneath the pie.
  • labeldistance: This parameter is the radial distance at which the pie labels are drawn.
  • startangle: This parameter is used to rotates the start of the pie chart by angle degrees counterclockwise from the x-axis.
  • radius: This parameter is the radius of the pie.
  • counterclock: This parameter specifies fractions direction, clockwise or counterclockwise.
  • wedgeprops: This parameter is dict of arguments passed to the wedge objects making the pie.
  • textprops: This parameter is dict of arguments to pass to the text objects.
  • center: This parameter is the Center position of the chart.
  • frame: This parameter is used to plot axes frame with the chart if true.
  • rotatelabels: This parameter is used to rotate each label to the angle of the corresponding slice if true.

Returns: This returns the following:

  • patches: This returns the sequence of matplotlib.patches.Wedge instances.
  • texts: This returns the list of the label matplotlib.text.Text instances.
  • autotexts: This returns the list of Text instances for the numeric labels.

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

Example #1:

# Implementation of matplotlib function
import matplotlib.pyplot as plt
labels = 'Geek1', 'Geek2', 'Geek3', 'Geek4'
sizes = [10, 20, 30, 40]
explode = (0.1, 0, 0, 0)
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode = explode,
        labels = labels, autopct ='% 1.1f %%',
        shadow = True, startangle = 90)
ax1.set_title('matplotlib.axes.Axes.pie Example')


Example #2:

# Implementation of matplotlib function
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots()
size = 0.3
vals = np.array([[90, 43], [57, 60],
                 [92, 20]])
cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
mid_colors = cmap(np.array([1, 2, 3, 4, 5, ]))
inner_colors = cmap(np.array([4, 12, 5
                              6, 9, 10]))
ax.pie(vals.sum(axis = 1), radius = 1
       colors = outer_colors,
       wedgeprops = dict(width = size, 
                         edgecolor ='w'))
ax.pie(vals.flatten(), radius = 1-size, 
       colors = mid_colors,
       wedgeprops = dict(width = size,
                         edgecolor ='w'))
ax.pie(vals.flatten(), radius = 1-2 * size,
       colors = inner_colors,
       wedgeprops = dict(width = size, 
                         edgecolor ='w'))
ax.set_title('matplotlib.axes.Axes.pie Example')

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course

My Personal Notes arrow_drop_up
Recommended Articles
Page :