Matplotlib Pie Charts

last modified February 25, 2025

Matplotlib is a powerful Python library for creating static, animated, and interactive visualizations. Pie charts are used to visualize the proportion of categories in a dataset. This tutorial covers how to create various types of pie charts using Matplotlib.

Pie charts are ideal for showing the relative sizes of categories as parts of a whole. Matplotlib provides a flexible and easy-to-use interface for creating pie charts with customizations.

Basic Pie Chart

This example demonstrates how to create a basic pie chart.

basic_pie_chart.py

import matplotlib.pyplot as plt

# Data
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

# Create a pie chart
plt.pie(sizes, labels=labels)

# Add a title
plt.title("Basic Pie Chart")

# Display the chart
plt.show()

The plt.pie() function is used to create a pie chart. The labels parameter assigns labels to each slice.

Customizing Pie Charts

This example demonstrates how to customize pie charts with colors, explode, and shadow effects.

custom_pie_chart.py

import matplotlib.pyplot as plt

# Data
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]
colors = ['gold', 'lightcoral', 'lightskyblue', 'lightgreen']
explode = (0.1, 0, 0, 0)  # "Explode" the first slice

# Create a pie chart with custom styles
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
        shadow=True, startangle=90)

# Add a title
plt.title("Custom Pie Chart")

# Display the chart
plt.show()

The explode, colors, shadow, and startangle parameters are used to customize the appearance of the pie chart.

Pie Chart with Percentages

This example shows how to display percentages on each slice of the pie chart.

pie_chart_with_percentages.py

import matplotlib.pyplot as plt

# Data
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

# Create a pie chart with percentages
plt.pie(sizes, labels=labels, autopct='%1.1f%%')

# Add a title
plt.title("Pie Chart with Percentages")

# Display the chart
plt.show()

The autopct parameter is used to display percentages on each slice.

Donut Chart

This example demonstrates how to create a donut chart.

donut_chart.py

import matplotlib.pyplot as plt

# Data
labels = ['A', 'B', 'C', 'D']
sizes = [15, 30, 45, 10]

# Create a pie chart
plt.pie(sizes, labels=labels, startangle=90)

# Draw a circle at the center to create a donut chart
centre_circle = plt.Circle((0, 0), 0.7, color='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

# Add a title
plt.title("Donut Chart")

# Display the chart
plt.show()

The plt.Circle() function is used to draw a white circle at the center of the pie chart, creating a donut chart.

Nested Pie Chart

This example demonstrates how to create a nested pie chart.

nested_pie_chart.py

import matplotlib.pyplot as plt

# Data for the outer pie
outer_labels = ['A', 'B', 'C', 'D']
outer_sizes = [15, 30, 45, 10]

# Data for the inner pie
inner_labels = ['X', 'Y', 'Z']
inner_sizes = [25, 35, 40]

# Create the outer pie chart
plt.pie(outer_sizes, labels=outer_labels, radius=1.2,
        wedgeprops=dict(width=0.3, edgecolor='w'))

# Create the inner pie chart
plt.pie(inner_sizes, labels=inner_labels, radius=0.8,
        wedgeprops=dict(width=0.4, edgecolor='w'))

# Add a title
plt.title("Nested Pie Chart")

# Display the chart
plt.show()

The radius and wedgeprops parameters are used to create a nested pie chart.

Best Practices for Pie Charts

Limit Categories: Use pie charts for datasets with a small number of categories.
Use Percentages: Display percentages to make the chart easier to interpret.
Avoid Overlapping Labels: Ensure labels do not overlap by adjusting the chart size or using legends.
Use Explode for Emphasis: Use the explode parameter to highlight specific slices.

Source

Matplotlib Pie Chart Documentation

In this article, we have explored various types of pie charts using Matplotlib, including basic, customized, donut, and nested pie charts.

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My name is Jan Bodnar and I am a passionate programmer with many years of programming experience. I have been writing programming articles since 2007. So far, I have written over 1400 articles and 8 e-books. I have over eight years of experience in teaching programming.

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