Font size guidelines in Matplotlib - Time & Space Complexity

Time Complexity: Font size guidelines

O(n)

Understanding Time Complexity

We want to understand how the time it takes to set font sizes in matplotlib changes as we add more text elements.

How does increasing the number of text labels affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

n = 10  # Example value for n
fig, ax = plt.subplots()
labels = [f"Label {i}" for i in range(n)]
for i, label in enumerate(labels):
    ax.text(0.5, i * 0.1, label, fontsize=12)
plt.show()

This code adds n text labels to a plot, each with a set font size.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Adding a text label with a font size setting inside a loop.
• How many times: The loop runs n times, once for each label.

How Execution Grows With Input

Each new label requires matplotlib to process font size and position, so the work grows as we add more labels.

Input Size (n)	Approx. Operations
10	10 font size settings and text placements
100	100 font size settings and text placements
1000	1000 font size settings and text placements

Pattern observation: The work increases directly with the number of labels added.

Final Time Complexity

Time Complexity: O(n)

This means the time to set font sizes grows linearly as we add more text labels.

Common Mistake

[X] Wrong: "Setting font size once applies to all labels automatically, so adding more labels doesn't add work."

[OK] Correct: Each label is a separate text object, so matplotlib must apply font size settings individually, increasing work with each label.

Interview Connect

Understanding how adding more text elements affects rendering time helps you explain performance considerations clearly and confidently.

Self-Check

"What if we set the font size globally once instead of per label? How would the time complexity change?"