Subplots for multiple charts in Data Analysis Python - Time & Space Complexity

Time Complexity: Subplots for multiple charts

O(n)

Understanding Time Complexity

When we create multiple charts using subplots, we want to know how the time to draw them grows as we add more charts.

We ask: How does the work increase when we add more charts to the subplot?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt
import numpy as np

n = 5  # number of charts
x = np.linspace(0, 10, 1000)
fig, axs = plt.subplots(n, 1)
for i in range(n):
    y = np.sin(x + i)
    axs[i].plot(x, y)
plt.show()

This code creates 5 charts stacked vertically, each plotting a sine wave shifted by i.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Loop over n charts to plot data.
• How many times: Exactly n times, once per chart.

How Execution Grows With Input

Each new chart adds a similar amount of work to draw the plot.

Input Size (n)	Approx. Operations
10	10 times the work of one chart
100	100 times the work of one chart
1000	1000 times the work of one chart

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

Final Time Complexity

Time Complexity: O(n)

This means the time to create all charts grows in a straight line as you add more charts.

Common Mistake

[X] Wrong: "Adding more charts won't increase time much because they are drawn together."

[OK] Correct: Each chart requires its own drawing steps, so more charts mean more work.

Interview Connect

Understanding how work grows with more charts helps you explain performance when visualizing data in real projects.

Self-Check

What if we plotted all lines on a single chart instead of multiple subplots? How would the time complexity change?