Unequal subplot sizes in Matplotlib - Time & Space Complexity

Time Complexity: Unequal subplot sizes

O(n)

Understanding Time Complexity

We want to understand how the time to create plots changes when we use subplots of different sizes in matplotlib.

How does the number and size of subplots affect the work matplotlib does?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.1, 0.6, 0.8])  # Large subplot
ax2 = fig.add_axes([0.75, 0.1, 0.2, 0.3]) # Smaller subplot

ax1.plot(range(100))
ax2.plot(range(50))
plt.show()
    

This code creates a figure with two subplots of different sizes and plots data on each.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Plotting data points on each subplot.
• How many times: Once per data point in each subplot (100 points in first, 50 in second).

How Execution Grows With Input

As the number of points to plot increases, the work grows roughly in direct proportion.

Input Size (n)	Approx. Operations
10	About 10 operations per subplot
100	About 100 operations per subplot
1000	About 1000 operations per subplot

Pattern observation: Doubling the number of points roughly doubles the work needed to plot.

Final Time Complexity

Time Complexity: O(n)

This means the time to draw the plots grows linearly with the number of points plotted.

Common Mistake

[X] Wrong: "Making one subplot bigger will make the whole plotting time much slower regardless of data size."

[OK] Correct: The size of the subplot affects drawing area but the main time cost comes from how many data points are plotted, not the subplot size alone.

Interview Connect

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

Self-Check

"What if we added a third subplot with twice as many points as the largest one? How would the time complexity change?"