Fig, ax = plt.subplots pattern in Matplotlib - Time & Space Complexity

Time Complexity: Fig, ax = plt.subplots pattern

O(n)

Understanding Time Complexity

We want to understand how the time it takes to create plots with plt.subplots changes as we increase the number of plots.

How does adding more subplots affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

fig, axs = plt.subplots(3, 3)
for i, ax in enumerate(axs.flat):
    ax.plot([0, 1, 2], [i, i+1, i+2])
plt.show()

This code creates a 3 by 3 grid of plots and draws a simple line on each subplot.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping over each subplot to draw a line.
• How many times: Once for each subplot, here 9 times (3 rows x 3 columns).

How Execution Grows With Input

As the number of subplots increases, the time to draw each line grows proportionally.

Input Size (n)	Approx. Operations
10 subplots	10 line plots drawn
100 subplots	100 line plots drawn
1000 subplots	1000 line plots drawn

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

Final Time Complexity

Time Complexity: O(n)

This means the time to create and draw on subplots grows linearly with how many subplots you have.

Common Mistake

[X] Wrong: "Creating multiple subplots happens instantly no matter how many there are."

[OK] Correct: Each subplot requires drawing operations, so more subplots mean more work and more time.

Interview Connect

Understanding how plotting scales helps you write efficient code when working with many charts or dashboards.

Self-Check

"What if we added nested loops to plot multiple lines per subplot? How would the time complexity change?"