Python DSA Mastery — Part 2

Amolkharat

11 min read

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Oct 5, 2026

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Big-O Notation in Python: Finally Understand Time & Space Complexity

Your code works. But can it survive 1 million inputs? Big-O tells you the answer.

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Imagine you are asked to find a name in a list.

You write:

names = ["Amol", "Rahul", "Priya", "Sneha", "Vijay"]

for name in names:
if name == "Priya":
    print("Found")

It works perfectly.

Now imagine the list contains:

10 people
1,000 people
1,000,000 people
100,000,000 people

The code hasn’t changed.

But the amount of work it may need to perform has changed dramatically.

So the real question isn’t:

“Does my code work?”

It’s:

“How does my code behave when the input becomes huge?”

That is the problem Big-O notation helps us understand.

🧠 What Is Big-O?

Big-O notation describes how the time or memory requirements of an algorithm grow as the input size grows.

We usually represent input size using:

n