Grouped bar charts in Matplotlib - Time & Space Complexity

Time Complexity: Grouped bar charts

O(n)

Understanding Time Complexity

When we create grouped bar charts with matplotlib, we want to know how the time to draw the chart changes as we add more groups or bars.

We ask: How does the drawing time grow when the data size grows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import matplotlib.pyplot as plt

labels = ['A', 'B', 'C', 'D']
values1 = [5, 7, 3, 4]
values2 = [6, 8, 4, 5]

x = range(len(labels))
width = 0.35

plt.bar([i - width/2 for i in x], values1, width=width, label='Group 1')
plt.bar([i + width/2 for i in x], values2, width=width, label='Group 2')
plt.legend()
plt.show()

This code draws two groups of bars side by side for each label.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Drawing bars for each group and label.
• How many times: Once per bar, so number of groups times number of labels.

How Execution Grows With Input

As the number of labels or groups grows, the number of bars to draw grows too.

Input Size (n = labels x groups)	Approx. Operations
10 (e.g., 5 labels x 2 groups)	About 10 bar draws
100 (e.g., 50 labels x 2 groups)	About 100 bar draws
1000 (e.g., 500 labels x 2 groups)	About 1000 bar draws

Pattern observation: The drawing work grows directly with the total number of bars.

Final Time Complexity

Time Complexity: O(n)

This means the time to draw grows in a straight line with the number of bars.

Common Mistake

[X] Wrong: "Adding more groups does not affect drawing time much because bars are drawn together."

[OK] Correct: Each bar is drawn separately, so more groups mean more bars and more drawing steps.

Interview Connect

Understanding how drawing time grows helps you explain performance when visualizing large datasets, a useful skill in data science roles.

Self-Check

"What if we added a third group of bars? How would the time complexity change?"