L2 · Working engineerPython internals~6 min · 5 tests#61

Build a Custom Iterator (iter, next)

Implement Python's iterator protocol with __iter__ and __next__, raising StopIteration at the end. Your class then works in for-loops and list().

The problem

Write a class MyIterator(data) that walks a list using the iterator protocol:

__iter__ returns the iterator itself
__next__ returns the next item, and raises StopIteration when finished

list(MyIterator([10, 20, 30])) → [10, 20, 30].

Examples

Example 1

Input

list(MyIterator([10, 20, 30]))

Expected output

[10, 20, 30]

Example 2

Input

(lambda it: iter(it) is it)(MyIterator([1]))

Expected output

True

+ 3 hidden tests on Submit — StopIteration at the end.

Edge cases to ask about

Empty data
Exhaustion
Reuse after exhaustion

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 · MyIterator

class MyIterator:
    def __init__(self, data):
        self.data = data
        self.index = 0

    def __iter__(self):
        pass

    def __next__(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
Iterator protocol	O(1) per item	O(1)	for-loops call iter() then next() until StopIteration.

Walkthrough of the optimal approach (try it yourself first)

A for loop calls iter(obj) once, then next() until StopIteration. An iterator returns self from __iter__ and keeps its position.

An iterable (like a list) returns a new iterator from __iter__, which is why you can loop over a list twice but this iterator only once. A generator function is the shortcut that writes this class for you.

Complexity: O(1) time, O(1) space. Each next() does constant work and the iterator stores one index.

Reveal the reference solution

class MyIterator:
    def __init__(self, data):
        self.data = data
        self.index = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.index >= len(self.data):
            raise StopIteration
        value = self.data[self.index]
        self.index += 1
        return value

Follow-ups interviewers ask

Make it re-iterable (an iterable that returns a fresh iterator).
Rewrite it as a generator.

Frequently asked interview questions

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

What is the time complexity of Build a Custom Iterator (__iter__, __next__) in Python?

The optimal solution runs in O(1) time and O(1) auxiliary space. Each next() does constant work and the iterator stores one index.

What follow-up questions do interviewers ask about Build a Custom Iterator (__iter__, __next__)?

Make it re-iterable (an iterable that returns a fresh iterator). Rewrite it as a generator.