zip() function in Python - Time & Space Complexity

Time Complexity: zip() function

O(n)

Understanding Time Complexity

Let's explore how the time needed to run the zip() function changes as the input lists get bigger.

We want to know how the work done grows when we combine multiple lists using zip().

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

list1 = [1, 2, 3, 4, 5]
list2 = ['a', 'b', 'c', 'd', 'e']

zipped = list(zip(list1, list2))
print(zipped)

This code pairs elements from two lists into tuples, creating a new list of these pairs.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Iterating through both lists at the same time.
• How many times: Once for each element in the shortest list.

How Execution Grows With Input

As the lists get longer, zip() goes through each element once, pairing them up.

Input Size (n)	Approx. Operations
10	About 10 pairs created
100	About 100 pairs created
1000	About 1000 pairs created

Pattern observation: The work grows directly with the number of elements; double the elements, double the work.

Final Time Complexity

Time Complexity: O(n)

This means the time to run zip() grows in a straight line with the size of the input lists.

Common Mistake

[X] Wrong: "zip() takes the same time no matter how big the lists are."

[OK] Correct: Actually, zip() must look at each element to pair them, so bigger lists take more time.

Interview Connect

Understanding how zip() scales helps you explain how combining data works efficiently, a useful skill in many coding tasks.

Self-Check

What if we zipped three or more lists instead of two? How would the time complexity change?