Creating and Customizing Pie Charts with Matplotlib in Python

Pie charts might not be the most advanced data visualization tool, but they serve a simple purpose: to show proportions at a glance. Whether you’re analyzing sales figures, poll results, or survey outcomes, a clean pie chart communicates the point effectively. In Python, the Matplotlib library makes it straightforward to generate and customize these charts to suit your needs.

Creating a Basic Pie Chart Using Matplotlib

Matplotlib, one of the most stable plotting libraries in Python, offers a simple function called pie() to draw pie charts. Before diving into code, ensure Matplotlib is installed:

pip install matplotlib

Here’s how to create a basic pie chart:

import matplotlib.pyplot as plt

labels = ['Apples', 'Bananas', 'Cherries', 'Dates']
sizes = [30, 25, 25, 20]

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

This generates a default pie chart with labeled slices. For added clarity, you can display percentages directly on the chart:

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

The autopct parameter formats the percentages to one decimal place, ensuring quick readability.

How to Customize Pie Charts for Better Readability?

Once you have a basic chart, you can customize its appearance for clarity and emphasis. Use the colors parameter to change slice colors:

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

To highlight a specific slice, such as emphasizing ‘Apples’, use the explode parameter:

explode = (0.1, 0, 0, 0)  # Emphasize the first slice
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%')

Adding a shadow or adjusting the start angle can also enhance readability:

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

For a donut-style chart, add a circle at the center:

wedges, texts, autotexts = plt.pie(sizes, labels=labels, autopct='%1.1f%%')
centre_circle = plt.Circle((0, 0), 0.70, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

Working with Data Sources and Dynamic Plotting

Pie charts are often generated from datasets rather than hardcoded values. Using Pandas, you can create a pie chart from a dataset:

import pandas as pd

data = {
    'Fruit': ['Apples', 'Bananas', 'Cherries', 'Dates'],
    'Sales': [300, 250, 250, 200]
}

df = pd.DataFrame(data)
plt.pie(df['Sales'], labels=df['Fruit'], autopct='%1.1f%%')
plt.title('Sales Distribution by Fruit')
plt.show()

For integration into dashboards or automated reports, save your chart instead of displaying it:

plt.savefig('pie_chart.png')

Comparing Pie Charts with Other Chart Types in Python

When creating data visualizations, selecting the right chart type is crucial. Pie charts are excellent for simple breakdowns with a few categories, but as segments increase, readability declines. In such cases, bar charts offer better clarity. For trends over time, line charts provide more insight. While Matplotlib’s pie chart capabilities are robust, sometimes switching to a bar or line chart enhances clarity.

Conclusion

Pie charts are not about showing everything—they’re about emphasizing the essentials. When applied effectively, they bring out stories that might be buried in a table or lost in a bar chart. With Matplotlib, crafting a Python pie chart is quick and doesn’t compromise on style or readability. Whether it’s a report, business review, or dashboard summary, a well-placed pie chart delivers your message cleanly. Keep it simple, keep it readable, and let the proportions do the talking. That’s the power of effective data visualization.