Donut chart variation in Matplotlib - Time & Space Complexity

Time Complexity: Donut chart variation

O(n)

Understanding Time Complexity

We want to understand how the time to draw a donut chart changes as the amount of data grows.

How does adding more slices affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

sizes = [15, 30, 45, 10]
labels = ['A', 'B', 'C', 'D']

plt.pie(sizes, labels=labels, wedgeprops={'width':0.3})
plt.show()

This code draws a donut chart with 4 slices, each slice sized by the values in sizes.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Drawing each slice of the donut chart.
• How many times: Once for each slice in the sizes list.

How Execution Grows With Input

As the number of slices increases, the work to draw each slice adds up.

Input Size (n)	Approx. Operations
10	10 drawing steps
100	100 drawing steps
1000	1000 drawing steps

Pattern observation: The work grows directly with the number of slices.

Final Time Complexity

Time Complexity: O(n)

This means the time to draw the donut chart grows in a straight line as you add more slices.

Common Mistake

[X] Wrong: "Drawing a donut chart always takes the same time no matter how many slices there are."

[OK] Correct: Each slice requires separate drawing steps, so more slices mean more work.

Interview Connect

Understanding how drawing time grows with data size helps you explain performance in data visualization tasks clearly and confidently.

Self-Check

"What if we added animation to the donut chart? How would that affect the time complexity?"