L2 · Working engineerStacks & queues~8 min · 4 tests#46

Implement a Stack Using Queues

Build a LIFO stack using only FIFO queue operations (deque append/popleft). Compare push-heavy and pop-heavy designs and their O(n) trade-off.

The problem

Implement MyStack using only queue operations on collections.deque: append (to the back), popleft (from the front) and len.

push(x)
pop() — returns the top, or None if empty

Examples

Example 1

Input

run_ops(MyStack, [], [["push", 10], ["push", 20], ["push", 30], ["pop"]])

Expected output

[None, None, None, 30]

Example 2

Input

run_ops(MyStack, [], [["push", 1], ["push", 2], ["pop"], ["pop"], ["pop"]])

Expected output

[None, None, 2, 1, None]

+ 2 hidden tests on Submit.

Edge cases to ask about

Pop from empty
Interleaved operations

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 · MyStack

from collections import deque

class MyStack:
    def __init__(self):
        self.q = deque()

    def push(self, x):
        pass

    def pop(self):
        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 read why.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

From brute force to optimal

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

Approach	Time	Space	Idea
Costly push (rotate new item to front)	O(n) push, O(1) pop	O(n)	One queue is enough.
bestCostly pop (move n−1 items to a second queue)	O(1) push, O(n) pop	O(n)	The classic two-queue version.

Walkthrough of the optimal approach (try it yourself first)

After appending a new item, rotate the queue len − 1 times (popleft + append) so the new item sits at the front. Then pop is a plain popleft.

Push is O(n) and pop is O(1). The two-queue version flips that trade-off — the interviewer may ask which you would choose for a push-heavy workload.

Complexity: O(n) time, O(n) space. With the rotate-on-push design, push re-queues the n − 1 older items; pop is O(1).

Reveal the reference solution

from collections import deque

class MyStack:
    def __init__(self):
        self.q = deque()

    def push(self, x):
        self.q.append(x)
        for _ in range(len(self.q) - 1):     # rotate x to the front
            self.q.append(self.q.popleft())

    def pop(self):
        return self.q.popleft() if self.q else None

Follow-ups interviewers ask

Make push O(1) and pop O(n) instead.
Add top().

Frequently asked interview questions

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

What is the time complexity of Implement a Stack Using Queues in Python?

The optimal solution runs in O(n) time and O(n) auxiliary space. With the rotate-on-push design, push re-queues the n − 1 older items; pop is O(1).

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

Costly push (rotate new item to front): O(n) push, O(1) pop time, O(n) space. One queue is enough. Costly pop (move n−1 items to a second queue): O(1) push, O(n) pop time, O(n) space. The classic two-queue version.

What follow-up questions do interviewers ask about Implement a Stack Using Queues?

Make push O(1) and pop O(n) instead. Add top().