Nested pie charts in Matplotlib

Matplotlib is a Python library best used for visualizing various forms of data. This library contains a vast set of charts to achieve this purpose including pie charts. Aside from the traditional pie chart, we can use Matplotlib to go a step further and create nested pie charts to accurately demonstrate subdivisions. In this Answer, we'll look at implementing nested pie charts in Matplotlib.

Pie charts

Pie charts are an excellent way to represent data pictorially. They are perhaps one of the most straightforward and self-explanatory data visualization techniques.

Various definitions

Formally speaking, a pie chart, also called a circle chart, is a circular entity or a pie where different slices of the pie represent the division of data.
Simply put, different portions of the circle show different sections of our data.

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.5

dataset = np.array([[20., 42.], [64., 92.], [34., 56.]]) 

colorset = plt.colormaps["Dark2"]
outer = colorset([0, 4, 8])

inner = colorset([1, 2, 3, 5, 6, 7, 9, 10, 11])

ax.pie(dataset.sum(axis=1), radius=1, colors=outer, wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(dataset.flatten(), radius=1-size, colors=inner, wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect='equal', title='Nested Pie Chart') 

plt.show()

Code explanation

Lines 1–2: Prior to writing our main code, we first import matplotlib.pyplot with an alias name of plt for plotting. We then import numpy with an alias name of np for numerical operations.
Line 4: First, we use plt.subplots() to generate a figure and an axis object, and assign them fig and ax variables respectively.
Line 6: The size is then set to 0.5, which represents the width of the wedge in the pie chart.
Line 8: We create a numpy array, i.e., dataset, and assign 2D array values to it.
Line 10: For our aesthetics regarding the piechart, we use the color Dark2 set from colormaps and assign it to the colorset variable.
Line 11: We take the color shades depicted by [0, 4, 8] from our chosen color palette for our outer main categories and assign it to outer.
Line 13: Moving towards our inner circle now, we specify the color shades corresponding to [1, 2, 3, 5, 6, 7, 9, 10, 11] from the colorset for the inner pie and assign it to inner.
and set it on the inner variable.
Line 15: We call the ax.pie function to create the outer pie chart and pass the sum of values along each row of the dataset, radius i.e., 1, colors i.e., outer, and the wedge properties wedgeprops i.e., width and edgecolor as parameters.
Line 17: We then use ax.pie function to create the inner pie chart and pass the flattened dataset values, radius i.e., 1 - size, colors i.e., inner, and the wedge properties wedgeprops i.e., width and edgecolor as parameters.
Line 19: Next, we use the ax.set function and pass the aspect ratio of the plot i.e., equal, and the title for the plot, i.e., "Nested Pie Chart" as parameters to set them respectively.
Line 21: Lastly, we call plt.show() to display our well-made pie chart!

Note: The dataset for the pie chart can be changed to reflect real life data and display the actual strength of pie charts.

Chart output

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.3

dataset = np.array([[20., 42.], [64., 92.], [34., 56.]]) 

colorset = plt.colormaps["Pastel1"]
outer_colors = colorset([0, 4, 8])

inner_colors = colorset([1, 2, 3, 5, 6, 7, 9, 10, 11])

outer_pie = ax.pie(dataset.sum(axis=1), radius=1, colors=outer_colors, wedgeprops=dict(width=size, edgecolor='w'), labels=['Category A', 'Category B', 'Category C'])

inner_pie = ax.pie(dataset.flatten(), radius=1-size, colors=inner_colors, wedgeprops=dict(width=size, edgecolor='w'))

ax.legend(outer_pie[0], ['Category A', 'Category B', 'Category C'], loc='upper left', title='Outer Categories')
ax.legend(inner_pie[0], ['Subcategory 1', 'Subcategory 2', 'Subcategory 3', 'Subcategory 4', 'Subcategory 5', 'Subcategory 6', 'Subcategory 7', 'Subcategory 8', 'Subcategory 9'][:len(dataset.flatten())], loc='upper right', title='Inner Subcategories')

ax.set(aspect='equal', title='Nested Pie Chart')

plt.show()

Use cases of nested pie charts

Nested pie charts are used to visualize hierarchical data with multiple levels of categories and subcategories.
We can use them to help show the proportion of each subcategory within the parent category.
Nested pie charts allow us to compare the distribution of subcategories across different categories easily.
They are primarily used in business, finance, and marketing to analyze hierarchical data relationships.
These strengths allow nested pie charts to aid in decision-making as well.

Keywords used in matplotlib's pie charts

Properties	Explanation
radius	Specifies the radius of the pie chart
colors	Defines the color scheme for the slices of the pie
wedgeprops	Sets the properties for the wedges, like width or edge color
labels	Indicates the category names
startangle	Determines the starting angle of the first pie chart's slice
shadow	Adds a shadow effect to the pie chart
legend	Displays a legend to show the categories of the pie chart
title	Gives a title / heading to the pie chart
aspect	Ensures that the pie chart is a circle, and maintains the aspect ratio

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Nested pie charts in Matplotlib

Pie charts

Various definitions

Basics of pie charts in matplotlib