Named colors and hex codes in Matplotlib - Time & Space Complexity

Time Complexity: Named colors and hex codes

O(n)

Understanding Time Complexity

We want to understand how the time it takes to use named colors or hex codes in matplotlib changes as we add more colors.

How does the number of colors affect the drawing time?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

colors = ['red', '#00FF00', 'blue', '#FF00FF', 'cyan']
for i, color in enumerate(colors):
    plt.plot([0, 1], [i, i], color=color)
plt.show()

This code draws several lines, each with a different color given by a named color or a hex code.

Identify Repeating Operations

• Primary operation: Looping over the list of colors to draw each line.
• How many times: Once for each color in the list.

How Execution Grows With Input

Each added color means one more line to draw, so the work grows steadily with the number of colors.

Input Size (n)	Approx. Operations
10	10 lines drawn
100	100 lines drawn
1000	1000 lines drawn

Pattern observation: The time grows directly in proportion to the number of colors.

Final Time Complexity

Time Complexity: O(n)

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

Common Mistake

[X] Wrong: "Using hex codes is slower than named colors because hex codes are longer strings."

[OK] Correct: Matplotlib treats both named colors and hex codes similarly internally, so the length of the color code does not affect drawing time noticeably.

Interview Connect

Understanding how the number of items affects drawing time helps you reason about performance in data visualization tasks, a useful skill in many projects.

Self-Check

What if we changed the code to draw multiple lines per color? How would the time complexity change?