How to Create Pie Charts with Matplotlib

Hey there! Today, I’m diving into one of the most popular (and delicious) tools for data visualization: the pie chart. Whether you’re a data newbie or a seasoned coder, pie charts are a fantastic way to represent proportions. Let’s walk through a basic example and then level it up with some practical enhancements. By the end, you’ll see how small tweaks can transform a simple chart into a polished, presentation-ready visual.

The Original Code A Simple Pie Chart

Let’s start with the basics. Here’s a minimal example using Python’s matplotlib:

import matplotlib.pyplot as plt

sizes = [40, 30, 20, 10]
labels = ['A', 'B', 'C', 'D']

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

Breaking It Down

1. Libraries: We import matplotlib.pyplot (aliased as plt), the go-to library for plotting. While numpy is imported here, it’s not actually used in this snippet—it’s often included for numerical workflows.
2. Data:
   • sizes: A list of values representing each slice’s proportion (here, percentages).
   • labels: Text labels for each slice.
3. Plotting:
   • plt.pie() generates the chart. The autopct parameter formats percentage labels (one decimal place).
   • plt.show() displays the chart.

This code works, but the result is pretty vanilla. Let’s jazz it up!

A Sleeker, More Informative Pie Chart

Here’s the upgraded version with practical improvements:

import matplotlib.pyplot as plt

sizes = [40, 30, 20, 10]
labels = ['A', 'B', 'C', 'D']
explode = [0.1, 0, 0, 0]  # "Pop out" the first slice

plt.figure(figsize=(8, 8))  # Larger figure size

plt.pie(
    sizes,
    labels=labels,
    autopct='%1.1f%%',
    explode=explode,  # Highlight a slice
    startangle=90,    # Rotate chart for better orientation
    shadow=True       # Add depth with a shadow
)

plt.axis('equal')     # Ensure the pie is a circle
plt.title("Enhanced Pie Chart")  
plt.legend(labels, title="Categories", loc="upper right")  
plt.savefig("enhanced_pie_chart.png")  # Save as image
plt.show()

What’s New? Key Enhancements Explained

Let’s unpack the upgrades:

1. Exploding a Slice
   • explode = [0.1, 0, 0, 0] shifts the first slice outward by 10% of the radius. This draws attention to a specific category (like highlighting top revenue sources).
2. Custom Start Angle
   • startangle=90 rotates the chart so the first slice starts at the top (12 o’clock position). This avoids awkward orientations, like starting a dominant slice halfway off-screen.
3. Shadow Effect
   • shadow=True adds a subtle 3D-like drop shadow, making the chart visually appealing.
4. Title and Legend
   • A title (plt.title()) clarifies the chart’s purpose.
   • The legend (plt.legend()) maps labels to colors, which is especially helpful if slice labels are truncated or overlapping.
5. Saving the Figure
   • plt.savefig() exports the chart as a PNG (or other formats) for use in reports or slides.
6. Equal Aspect Ratio
   • plt.axis('equal') ensures the pie isn’t distorted into an ellipse—a common pitfall when resizing figures.

Why Do These Tweaks Matter?

• Clarity: Labels, legends, and titles make the chart self-explanatory.
• Focus: Exploding a slice directs viewers to key data points.
• Professionalism: Shadows and proper sizing ensure the chart looks polished in presentations.

Final Thought

A pie chart is more than just a circle divided into slices—it’s a storytelling tool. While the basic version gets the job done, thoughtful enhancements turn it into a communication powerhouse. By adding context (titles, legends), improving readability (exploding slices), and embracing aesthetics (shadows, angles), your charts will not only inform but also engage your audience.