Inline display in Jupyter notebooks in Matplotlib - Time & Space Complexity

Time Complexity: Inline display in Jupyter notebooks

O(n)

Understanding Time Complexity

When we use matplotlib to show plots inside Jupyter notebooks, the way plots are displayed affects how long it takes to show them.

We want to understand how the time to display grows as we create more or bigger plots.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

%matplotlib inline
import matplotlib.pyplot as plt

for i in range(n):
    plt.plot([0, 1, 2], [i, i+1, i+2])
    plt.show()

This code creates and shows n small plots one after another inside the notebook.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The loop runs n times, each time creating and displaying one plot.
• How many times: The plot creation and display happen once per loop, so n times total.

How Execution Grows With Input

As n grows, the total time to create and show all plots grows roughly in direct proportion to n.

Input Size (n)	Approx. Operations
10	10 plot creations and displays
100	100 plot creations and displays
1000	1000 plot creations and displays

Pattern observation: Doubling n doubles the total work because each plot is handled separately.

Final Time Complexity

Time Complexity: O(n)

This means the time to display all plots grows linearly with the number of plots created.

Common Mistake

[X] Wrong: "Showing multiple plots inline happens instantly no matter how many plots there are."

[OK] Correct: Each plot requires time to render and display, so more plots mean more total time.

Interview Connect

Understanding how plotting time grows helps you write efficient data exploration code and explain performance in real projects.

Self-Check

"What if we create all plots first and call plt.show() only once at the end? How would the time complexity change?"