List comprehension vs loop in Python - Performance Comparison

Time Complexity: List comprehension vs loop

O(n)

Understanding Time Complexity

We want to see how the time it takes to create a list changes when using list comprehension versus a loop.

How does the method affect the speed as the list size grows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

numbers = [1, 2, 3, 4, 5]
squares_loop = []
for n in numbers:
    squares_loop.append(n * n)

squares_comp = [n * n for n in numbers]

This code creates a list of squares of numbers using a loop and a list comprehension.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Multiplying each number by itself and adding it to a list.
• How many times: Once for each number in the input list.

How Execution Grows With Input

Each number is processed one time, so the work grows directly with the list size.

Input Size (n)	Approx. Operations
10	10 multiplications and 10 append operations
100	100 multiplications and 100 append operations
1000	1000 multiplications and 1000 append operations

Pattern observation: The number of operations grows evenly as the list gets bigger.

Final Time Complexity

Time Complexity: O(n)

This means the time to create the new list grows in a straight line with the number of items.

Common Mistake

[X] Wrong: "List comprehension is always faster because it has better time complexity."

[OK] Correct: Both methods do the same number of steps, so their time complexity is the same; speed differences come from how Python runs them internally, not from complexity.

Interview Connect

Understanding how loops and list comprehensions scale helps you write clear and efficient code, a skill valued in many programming tasks.

Self-Check

"What if we used nested loops or nested list comprehensions? How would the time complexity change?"