L1 · FoundationsList basics~2 min · 4 tests

Linear Search in Python

Find the index of a target in an unsorted list by checking each element — O(n) linear search — and return -1 when it is missing. Run and time it live.

The problem

Return the index of the first occurrence of target in nums, or -1 if it is not there. Don't use list.index().

Examples

Example 1
Input
```
linear_search([4, 2, 7, 2], 2)
```
Expected output
```
1
```
Example 2
Input
```
linear_search([4, 2, 7], 9)
```
Expected output
```
-1
```

+ 2 hidden tests on Submit.

Edge cases to ask about

Missing target
Duplicates (first one wins)
Empty

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · linear_search

def linear_search(nums, target):
    """Index of the first target, or -1."""
    # Write your solution here
    pass

⌘/Ctrl + Enter runs the examples

Your code runs in real CPython inside your browser — nothing is sent anywhere. The first run downloads the interpreter (about 6 MB, once). Your code is saved on this device as you type.

Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and measure your code against the optimal one at growing input sizes.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

Measure it

Runs the function on inputs of size 250 up to 16,000 and records the time and peak memory. Slow solutions stop early — a short curve is itself the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
Check every element	O(n)	O(1)	Best case O(1), worst case O(n).

Walkthrough of the optimal approach (try it yourself first)

Walk the list with enumerate and return the first matching index. Best case O(1) (it is first), worst case O(n) (last or missing) — Big-O usually describes the worst case.

On an unsorted list, linear search is optimal. If the list is sorted, binary search (next question) is exponentially faster.

Complexity: O(n) time, O(1) space. In the worst case (missing or last) every element is checked.

Reveal the reference solution

def linear_search(nums, target):
    for i, x in enumerate(nums):
        if x == target:
            return i
    return -1

Follow-ups interviewers ask

Return every index of target.

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Linear Search in Python?

The optimal solution runs in O(n) time and O(1) auxiliary space. In the worst case (missing or last) every element is checked.

What follow-up questions do interviewers ask about Linear Search in Python?

Return every index of target.