Pie charts are a popular way to represent parts of a whole. They divide a circle into slices, where each slice’s size is proportional to the value it represents.

In this chapter, we’ll cover:

• Creating a basic pie chart
• Adding labels and percentages
• Exploding slices
• Customizing colors and start angles
• Creating a donut chart
• Using pie charts effectively

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1. What is a Pie Chart?

A pie chart shows how a total is divided into categories:

• Each slice represents a category
• Slice size corresponds to its percentage of the whole
• Useful for showing proportions

⚠️ Note: Pie charts should only be used when comparing a small number of categories. Too many slices make them hard to read.

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2. Basic Pie Chart

import matplotlib.pyplot as plt

# Data
sizes = [30, 25, 20, 15, 10]
labels = ["A", "B", "C", "D", "E"]

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

plt.title("Basic Pie Chart")
plt.show()

✅ A simple pie chart with slices representing categories.

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3. Adding Percentages

You can display percentages on each slice using autopct:

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

• %1.1f%% → one decimal place (e.g., 25.0%)

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4. Exploding (Highlighting) a Slice

To emphasize a category, you can “explode” a slice outward:

explode = [0.1, 0, 0, 0, 0]  # explode the first slice

plt.pie(sizes, labels=labels, autopct="%1.1f%%", explode=explode)
plt.title("Pie Chart with Exploded Slice")
plt.show()

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5. Customizing Colors and Start Angle

You can customize colors and rotate the starting angle:

colors = ['gold', 'lightblue', 'lightgreen', 'pink', 'orange']

plt.pie(sizes, labels=labels, autopct="%1.1f%%", colors=colors, startangle=90)
plt.title("Customized Pie Chart")
plt.show()

• startangle=90 rotates the chart to start from the top
• colors can be a list of color names or hex codes

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6. Donut Chart (Pie Chart with a Hole)

You can create a donut chart by adding a white circle at the center:

# Pie chart
plt.pie(sizes, labels=labels, autopct="%1.1f%%", colors=colors)

# Add a circle at the center
centre_circle = plt.Circle((0,0), 0.70, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

plt.title("Donut Chart")
plt.show()

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7. Multiple Pie Charts (Comparison)

Sometimes you may want to compare two groups:

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))

ax1.pie(sizes, labels=labels, autopct="%1.1f%%", colors=colors)
ax1.set_title("Group 1")

sizes2 = [25, 20, 30, 15, 10]
ax2.pie(sizes2, labels=labels, autopct="%1.1f%%", colors=colors)
ax2.set_title("Group 2")

plt.show()

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8. When to Use (and Avoid) Pie Charts

✅ Use Pie Charts When:

• Showing proportions of a whole
• Comparing few categories (max 5–6)
• Highlighting a single category

❌ Avoid Pie Charts When:

• Comparing too many categories
• Showing precise differences (use bar plots instead)

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✅ Summary

In this chapter, you learned how to:

• Create basic pie charts
• Add labels and percentages
• Highlight slices using explode
• Customize colors and angles
• Create donut charts and side-by-side comparisons
• Understand when pie charts are effective

Pie charts are great for quick visual impressions, but for detailed comparisons, bar plots often work better.

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👉 Up next: Chapter 7 – Stack Plots in Matplotlib