Pie Charts in Python with Matplotlib

Pie charts are great when you want to show how a whole is divided into smaller parts. Instead of comparing raw numbers, they help you quickly see which categories take up the biggest share.

Whether you're analyzing screen time, monthly expenses, vote shares, market share, or programming language popularity, a pie chart makes percentage-based data much easier to understand at a glance.

In this guide, you'll learn how to create pie charts in Python using Matplotlib, customize colors, add percentage labels, highlight important slices, and even turn a regular pie chart into a modern donut chart.

How plt.pie() works — in 5 steps

Basic pie chart example

Let's start with a simple example showing programming language popularity. This is one of the easiest ways to understand how Matplotlib converts raw values into slices and automatically calculates percentages for each category.

import matplotlib.pyplot as plt

labels = ['Python', 'Java', 'C++', 'JavaScript']
sizes  = [40, 25, 20, 15]

plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title('Programming Language Popularity')
plt.show()

Now a more interesting version — how long the top 6 internet memes have been famous:

import matplotlib.pyplot as plt

memes = [
    "Pepe the Frog",
    "Doge",
    "This Is Fine Dog",
    "Distracted Boyfriend",
    "Woman Yelling at a Cat",
    "Grumpy Cat"
]
years_famous = [17, 12, 12, 8, 6, 13]
colors = ['#9fcbf5', '#67a6e0', '#2b85d9', '#1862a8', '#1b4f80', '#073b6b']

plt.pie(years_famous, labels=memes, autopct='%1.1f%%',
        startangle=140, colors=colors)
plt.title("Top 6 Memes by Years of Popularity (2025)",
          fontdict={'fontsize': 15})
plt.show()

Output:

Key parameters explained

Everything you can drop inside plt.pie():

Explode a slice

Sometimes one category dominates the entire chart. Instead of letting it blend in with everything else, Matplotlib lets you pull that slice outward using the explode parameter.

It's a simple effect, but it immediately draws attention to the most important part of your data. Think of it as putting a spotlight on the slice you want readers to notice first.

explode = [0.1, 0, 0, 0, 0, 0]  # pulls out Pepe the Frog

plt.pie(years_famous, labels=memes, autopct='%1.1f%%',
        startangle=140, colors=colors, explode=explode)
plt.title("Pepe Leads — Explode Effect")
plt.show()

The value 0.1 means "pull this slice outward by 10% of the radius." Use 0.2 or 0.3 for a more dramatic separation. Every other slice stays at 0.

Move labels to a legend

When slice labels are long, they overlap on the chart. The cleaner solution is to remove inline labels and use plt.legend() instead:

# Remove labels= from plt.pie() — pass only sizes and styles
plt.pie(years_famous, autopct='%1.1f%%',
        startangle=140, colors=colors)

# Add legend outside the chart
plt.legend(memes,
           title="Memes",
           loc="upper left",
           bbox_to_anchor=(1, 1))

plt.tight_layout()  # prevents the legend from being clipped
plt.show()

• Remove labels=memes from plt.pie() — otherwise labels appear twice
• bbox_to_anchor=(1, 1) — anchors the legend box outside the chart area
• plt.tight_layout() — adjusts figure bounds so nothing gets cut off

Donut chart with wedgeprops

Regular pie charts work great, but donut charts often look cleaner and more modern. They show the same information while leaving extra space in the center for labels, totals, or additional insights.

In Matplotlib, converting a pie chart into a donut chart only requires one extra parameter. The wedgeprops argument controls the appearance of each slice and can create the hollow center that gives donut charts their signature look.

plt.pie(years_famous, labels=memes, autopct='%1.1f%%',
        startangle=140, colors=colors,
        wedgeprops=dict(width=0.65))

# Optional: add a label in the center hole
plt.text(0, 0, "Memes", ha='center', va='center',
         fontsize=13, fontweight='bold')

plt.title("Meme Longevity — Donut Chart")
plt.show()

width=0.65 means each wedge is 65% of the full radius — leaving 35% hollow. Lower the number for a thinner ring. The optional plt.text(0, 0, ...) places a label right in the center hole.

When to use a pie chart

Mini project — visualize your screen time

Your phone already has the data. Open your screen time or digital wellbeing settings, pull last week's numbers, and build a pie chart that shows exactly where your hours went.

Common pie chart mistakes beginners make

Pie charts are easy to create, but a few common mistakes can make them harder to read than they need to be.

• Using too many categories and creating tiny unreadable slices.
• Comparing values that are almost identical.
• Using similar colors that blend together.
• Exploding multiple slices at once.
• Forgetting percentage labels with autopct.
• Using a pie chart when a bar chart would communicate the data more clearly.

As a general rule, keep pie charts under seven categories and use them only when your data represents parts of a whole.

Frequently asked questions