L2 · Working engineerSets~3 min · 4 tests#37

Common Elements Using Sets

Use Python set intersection (&) to find common elements of two lists in O(n+m), returning them sorted. Learn set operators with live test cases.

The problem

Return the values found in both a and b, as a sorted list without duplicates. Use set operations.

Examples

Example 1

Input

common_sorted([1, 2, 3, 4], [3, 4, 5, 6])

Expected output

[3, 4]

Example 2
Input
```
common_sorted([9, 1, 9], [1, 9])
```
Expected output
```
[1, 9]
```

+ 2 hidden tests on Submit.

Edge cases to ask about

No overlap
Duplicates
Empty list

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 · common_sorted

def common_sorted(a, b):
    """Sorted list of values in both a and b."""
    # 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
Nested scans	O(n × m)	O(1)
bestSet intersection	O(n + m + k log k)	O(n + m)	& is O(min(n, m)); sorting the k results adds k log k.

Walkthrough of the optimal approach (try it yourself first)

set(a) & set(b) (or set(a).intersection(b)) gives the common values; sorted() makes the order deterministic. Know the four operators: | union, & intersection, - difference, ^ symmetric difference.

Complexity: O(n + m) time, O(n + m) space. Building both sets is linear and & is O(min(n, m)); sorting only the k common values adds k log k.

Reveal the reference solution

def common_sorted(a, b):
    return sorted(set(a) & set(b))

Follow-ups interviewers ask

Keep the order of a instead of sorting (question 8).

Frequently asked interview questions

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

What is the time complexity of Common Elements Using Sets in Python?

The optimal solution runs in O(n + m) time and O(n + m) auxiliary space. Building both sets is linear and & is O(min(n, m)); sorting only the k common values adds k log k.

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

Nested scans: O(n × m) time, O(1) space. Set intersection: O(n + m + k log k) time, O(n + m) space. & is O(min(n, m)); sorting the k results adds k log k.

What follow-up questions do interviewers ask about Common Elements Using Sets?

Keep the order of a instead of sorting (question 8).