Understanding Constant Time Complexity – O(1)

When you're learning Data Structures and Algorithms (DSA), one of the first concepts you'll hear about is time complexity — a way to describe how fast or slow a program runs depending on the size of the input.

Let’s start with the simplest and fastest one:

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What is Constant Time Complexity?

Constant Time Complexity, written as O(1), means:

No matter how big the input is, the operation takes the same amount of time to execute.

It doesn’t care if you’re working with 1 item or 1 million items it always takes just one step.

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Think of It Like This:

Imagine you have a locker with 1,000 drawers, and each drawer is labeled with a number from 1 to 1,000.

If someone asks you, “Give me the item in drawer 700,” and you go straight to drawer 700 and pull it out. You did it in one move.

That’s constant time — no searching, no looping. Just direct access.

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Simple Code Example in Python

def get_first_element(arr):
    return arr[0]

numbers = [10, 20, 30, 40, 50]
print(get_first_element(numbers))

Missed the earlier parts?
Make sure to check out the previous posts in this series for a complete understanding of time complexity and algorithm analysis:

• 🔗 DSA Introduction – Part 1

• 🔗 DSA Introduction – Part 2

• 🔗 DSA Introduction – Part 3

• 🔗 DSA Introduction – Part 4

• 🔗 DSA Introduction – Part 5

• 🔗 DSA Introduction – Part 6