L2 · Working engineerPython internals~3 min · 4 tests#67

is vs == in Python

Learn the difference between Python's is (same object) and == (equal value) with lists, None and small integers. Write it and prove it with tests.

The problem

Write compare(a, b) that returns a dict {"equal": …, "identical": …}:

"equal" — do they have the same value (==)?
"identical" — are they the same object (is)?

Examples

Example 1

Input

compare([1, 2, 3], [1, 2, 3])

Expected output

{'equal': True, 'identical': False}

Example 2

Input

(lambda x: compare(x, x))([1])

Expected output

{'equal': True, 'identical': True}

+ 2 hidden tests on Submit.

Edge cases to ask about

None
Same object
Small-int caching (never rely on it)

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 · compare

def compare(a, b):
    """Return {'equal': a == b, 'identical': a is 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 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
== calls __eq__; is compares identity	O(n) for ==, O(1) for is	O(1)

Walkthrough of the optimal approach (try it yourself first)

== calls __eq__ and compares values; is compares identity (same object). Two lists built separately are == but not is.

Use is only for singletons: x is None, x is True. Never use it for numbers or strings — 256 is 256 happens to be True because CPython caches small ints, but larger values may not be, so code that relies on it breaks unpredictably.

Complexity: O(1) time, O(1) space. `is` is a pointer comparison; `==` on lists compares element by element (O(n)).

Reveal the reference solution

def compare(a, b):
    return {"equal": a == b, "identical": a is b}

Follow-ups interviewers ask

Why is x is None preferred over x == None?
What does id() return?

Frequently asked interview questions

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

What is the time complexity of is vs == in Python?

The optimal solution runs in O(1) time and O(1) auxiliary space. is is a pointer comparison; == on lists compares element by element (O(n)).

What follow-up questions do interviewers ask about is vs == in Python?

Why is x is None preferred over x == None? What does id() return?