Pie charts are a popular way to visualize parts of a whole. Each slice of the pie represents a proportional segment of the data. Matplotlib makes it easy to create, style, and annotate pie charts for quick visual insights.

This guide covers the basics and advanced usage of matplotlib.pyplot.pie() with complete examples and expert tips.

What is a Pie Chart?

A pie chart divides a circle into slices to illustrate numerical proportions. It is most useful when you want to:

• Show percentage or relative proportions

• Compare parts of a whole

• Limit to a small number of categories (ideally < 6)

Creating a Basic Pie Chart

import matplotlib.pyplot as plt

labels = ['Apples', 'Bananas', 'Cherries', 'Dates']
sizes = [25, 30, 15, 30]

plt.pie(sizes, labels=labels)
plt.title('Fruit Distribution')
plt.show()

Adding Percentages with autopct

plt.pie(sizes, labels=labels, autopct='%1.1f%%')

• %1.1f%%: One decimal place

• %d%%: No decimals

Exploding a Slice (Offsetting)

Highlight one or more slices using explode:

explode = [0.1, 0, 0, 0]  # Offset first slice (Apples)

plt.pie(sizes, labels=labels, autopct='%1.1f%%', explode=explode)

Customizing Colors

colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')

Rotating and Start Angle

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)

Setting startangle=90 begins plotting from the top (12 o'clock), which is visually cleaner.

Shadow Effect

plt.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)

Legend for Clarity

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.legend(title="Fruits")

Full Example with All Features

import matplotlib.pyplot as plt

labels = ['Apples', 'Bananas', 'Cherries', 'Dates']
sizes = [25, 30, 15, 30]
colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
explode = (0.1, 0, 0, 0)  # explode 1st slice

plt.figure(figsize=(7, 7))
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90,
        colors=colors, explode=explode, shadow=True)

plt.title('Fruit Distribution')
plt.legend(loc='upper right')
plt.tight_layout()
plt.show()

Donut Chart (Pie Chart with Hole)

To create a donut chart, add a circle in the center:

fig, ax = plt.subplots()
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=colors)

# Draw circle
centre_circle = plt.Circle((0,0), 0.70, fc='white')
fig.gca().add_artist(centre_circle)

plt.title('Donut Chart Example')
plt.tight_layout()
plt.show()

Equal Aspect Ratio (Circle Shape)

Ensure the pie is drawn as a circle, not an ellipse:

plt.axis('equal')

✅ This is especially important when using custom figure sizes.

Pie Chart from a Dictionary

data = {'Python': 40, 'Java': 25, 'C++': 20, 'Ruby': 15}

labels = data.keys()
sizes = data.values()

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
plt.axis('equal')
plt.title("Programming Language Popularity")
plt.show()

Tips for Effective Pie Charts

⚠️ Common Pitfalls

Summary Table

✅ Final Complete Example

import matplotlib.pyplot as plt

data = {
    'Python': 45,
    'JavaScript': 30,
    'Java': 15,
    'C++': 10
}

labels = list(data.keys())
sizes = list(data.values())
colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
explode = [0.1, 0, 0, 0]

plt.figure(figsize=(8, 8))
plt.pie(sizes, labels=labels, colors=colors,
        autopct='%1.1f%%', startangle=140, explode=explode,
        shadow=True)

plt.axis('equal')
plt.title('Programming Language Usage in 2025')
plt.legend(loc='lower left')
plt.tight_layout()
plt.show()

Conclusion

Pie charts are best for simple, proportional comparisons where you need to show parts of a whole. With matplotlib.pyplot.pie(), you can style, label, and enhance your chart with just a few parameters.

What's Next?

• Try nested pie charts (multi-level)

• Explore interactive pie charts with Plotly

• Learn bar charts for categorical comparisons