L2 · Working engineerPython internals~3 min · 4 tests#69

Sort a List of Tuples by Second Element

Sort a list of (name, age) tuples by age with sorted() and a lambda key in O(n log n). Learn key functions, stability and operator.itemgetter.

The problem

data is a list of (name, age) tuples. Return it sorted by age, ascending. People with the same age keep their original order.

Examples

Example 1

Input

sort_by_age([('John', 30), ('Alice', 25), ('Bob', 28)])

Expected output

[('Alice', 25), ('Bob', 28), ('John', 30)]

Example 2

Input

sort_by_age([('A', 2), ('B', 1)])

Expected output

[('B', 1), ('A', 2)]

+ 2 hidden tests on Submit — stable on ties.

Edge cases to ask about

Ties
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 · sort_by_age

def sort_by_age(data):
    """Return data sorted by age (index 1), stable."""
    # Write your solution here
    pass

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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
sorted(key=…)	O(n log n)	O(n)	Timsort is stable, so ties keep input order.

Walkthrough of the optimal approach (try it yourself first)

sorted(data, key=lambda p: p[1]) sorts by the second element. Python's sort (Timsort) is stable, so equal ages keep their original order — that is what makes multi-key sorting by successive sorts possible.

operator.itemgetter(1) does the same and is slightly faster. Use reverse=True for descending.

Complexity: O(n log n) time, O(n) space. Timsort makes O(n log n) comparisons; sorted() returns a new list.

Reveal the reference solution

def sort_by_age(data):
    return sorted(data, key=lambda person: person[1])

Follow-ups interviewers ask

Sort by age descending, then name ascending.
Sort in place with list.sort.

Frequently asked interview questions

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

What is the time complexity of Sort a List of Tuples by Second Element in Python?

The optimal solution runs in O(n log n) time and O(n) auxiliary space. Timsort makes O(n log n) comparisons; sorted() returns a new list.

What follow-up questions do interviewers ask about Sort a List of Tuples by Second Element?

Sort by age descending, then name ascending. Sort in place with list.sort.