Matplotlib Pie Charts

Welcome to The Coding College, your go-to source for Python programming tutorials. In this tutorial, we’ll explore Pie Charts in Matplotlib—a popular way to visualize proportions or percentages in datasets. Pie charts are widely used in business, analytics, and presentations to showcase the composition of categories.

What Is a Pie Chart?

A pie chart divides data into slices to illustrate proportions. Each slice represents a category, with its size proportional to the category’s value relative to the whole.

Creating a Basic Pie Chart

To create a pie chart in Matplotlib, use the plt.pie() function.

Example: Basic Pie Chart

import matplotlib.pyplot as plt  

# Data
categories = ["Category A", "Category B", "Category C", "Category D"]  
values = [25, 35, 20, 20]  

# Create pie chart
plt.pie(values, labels=categories)  
plt.title("Basic Pie Chart")  
plt.show()

Output: A basic pie chart with four slices representing the proportions of each category.

Customizing Pie Charts

1. Adding Percentage Labels

To display percentages on the slices, use the autopct parameter:

plt.pie(values, labels=categories, autopct="%1.1f%%")  
plt.title("Pie Chart with Percentages")  
plt.show()

2. Customizing Colors

You can specify custom colors using the colors parameter:

colors = ["gold", "lightblue", "lightgreen", "pink"]  
plt.pie(values, labels=categories, autopct="%1.1f%%", colors=colors)  
plt.title("Pie Chart with Custom Colors")  
plt.show()

3. Highlighting a Slice

Use the explode parameter to separate a slice for emphasis:

explode = [0.1, 0, 0, 0]  # Highlight the first slice  
plt.pie(values, labels=categories, autopct="%1.1f%%", explode=explode, colors=colors)  
plt.title("Pie Chart with Highlighted Slice")  
plt.show()

4. Rotating the Chart

Use the startangle parameter to rotate the pie chart:

plt.pie(values, labels=categories, autopct="%1.1f%%", startangle=90, colors=colors)  
plt.title("Pie Chart with Rotation")  
plt.show()

Adding Legends

Legends help clarify the categories when the chart has many slices:

plt.pie(values, labels=categories, autopct="%1.1f%%", colors=colors)  
plt.legend(categories, title="Categories", loc="upper right")  
plt.title("Pie Chart with Legend")  
plt.show()

Creating a Donut Chart

A donut chart is a variation of a pie chart with a hole in the center. You can create it by setting a wedgeprops parameter:

plt.pie(values, labels=categories, autopct="%1.1f%%", colors=colors, wedgeprops={"width": 0.4})  
plt.title("Donut Chart")  
plt.show()

Exploding Multiple Slices

You can highlight multiple slices by modifying the explode list:

explode = [0.1, 0.1, 0, 0.1]  
plt.pie(values, labels=categories, autopct="%1.1f%%", explode=explode, colors=colors)  
plt.title("Pie Chart with Multiple Exploded Slices")  
plt.show()

Common Issues and Solutions

  1. Too Many Categories
    • Cause: Overcrowding slices in the chart.
    • Solution: Combine smaller categories into an “Others” category.
  2. Percentages Overlap
    • Cause: Small slices causing text overlap.
    • Solution: Use autopct="%1.1f%%" and adjust startangle for better spacing.
  3. Slice Colors Are Similar
    • Cause: Default color palette.
    • Solution: Use the colors parameter to define distinct colors.

Practice Exercises

Exercise 1: Highlight a Slice

Create a pie chart with five categories and highlight the category with the largest proportion.

Exercise 2: Create a Donut Chart

Convert a pie chart into a donut chart with customized width and colors.

Exercise 3: Combine Small Categories

Create a pie chart where smaller categories are grouped into an “Others” category.

Why Choose The Coding College?

At The Coding College, we aim to make complex concepts simple and practical. Learning pie charts in Matplotlib allows you to create visually compelling presentations, making your data analysis impactful and easy to understand.

Conclusion

Pie charts are a versatile and visually appealing way to represent proportions. With features like slice customization, annotations, and legends, Matplotlib enables you to create professional-grade visualizations effortlessly.

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