L1 · FoundationsList basics~2 min · 4 tests

Find Min and Max in One Pass

Return both the smallest and largest numbers of a list in a single loop as a tuple. Learn why one pass beats calling min() and max() separately.

The problem

Return a tuple (smallest, largest) from a non-empty list in one loop.

Examples

Example 1
Input
```
min_max([3, 9, 2, 7])
```
Expected output
```
(2, 9)
```
Example 2
Input
```
min_max([5])
```
Expected output
```
(5, 5)
```

+ 2 hidden tests on Submit.

Edge cases to ask about

Single element
All equal

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 · min_max

def min_max(nums):
    """Return (smallest, largest) in one pass."""
    # 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
min() then max()	O(n)	O(1)	Two passes, 2n comparisons.
bestOne loop	O(n)	O(1)	Same Big-O; one pass matters for streams.

Walkthrough of the optimal approach (try it yourself first)

Initialise both to the first element and update them in one loop. min() + max() is also O(n) — Big-O hides constant factors — but one pass matters when the data is a stream you can only read once.

Complexity: O(n) time, O(1) space. One pass; two variables.

Reveal the reference solution

def min_max(nums):
    lo = hi = nums[0]
    for x in nums[1:]:
        if x < lo:
            lo = x
        elif x > hi:
            hi = x
    return lo, hi

Follow-ups interviewers ask

Do it with about 1.5n comparisons (compare in pairs).

Frequently asked interview questions

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

What is the time complexity of Find Min and Max in One Pass in Python?

The optimal solution runs in O(n) time and O(1) auxiliary space. One pass; two variables.

What is the brute-force approach, and how do you optimise it?

min() then max(): O(n) time, O(1) space. Two passes, 2n comparisons. One loop: O(n) time, O(1) space. Same Big-O; one pass matters for streams.

What follow-up questions do interviewers ask about Find Min and Max in One Pass?

Do it with about 1.5n comparisons (compare in pairs).