What are mutable and immutable types in Python? | Interview Question

Q: What are mutable and immutable types in Python?

**Target Audience:** Beginners preparing for Python interviews — no prior Python knowledge assumed. **Difficulty:** Easy **Python version:** Behaviour checked on CPython 3.11 (python3.11 and the in-page Pyodide 0.25.1 runtime, which is CPython 3.11.3). > **How to read this doc:** Each idea is explained in plain language first, then tagged with `📌 Interview term:` so you also learn the exact word an interviewer expects. Every output shown in the **Verified** section was copied from a real run of the examples on this page. ## 1. Why This Even Matters — A Story First Picture a shared whiteboard and a printed poster in an office. Anyone can walk up to the whiteboard and add a line; everyone who looks at it afterwards sees that line, including people who never knew someone edited it. The poster cannot be edited at all. If you want a different poster, you print a new one and hang it in place of the old one, and anyone still holding the old poster sees the old text. Python objects are ei

The problem

Question presented to candidate: "Python talks a lot about mutable and immutable objects. What does that actually mean, which built-in types fall on each side, and where does the difference cause real bugs?"

What a strong answer should cover:

Mutable objects can change in place (list, dict, set, bytearray, most class instances); immutable ones cannot (int, float, bool, complex, str, bytes, tuple, frozenset, range, None), so every "change" builds a new object.
Assignment binds a name to an object and never copies, so two names can alias one mutable object; rebinding a name is not mutating an object.
+= mutates a list in place but rebinds a name for a tuple or int; id() / is prove which happened.
Immutability is shallow: a tuple can hold a list, and is then unhashable; the t[0] += [x] gotcha both mutates and raises.
Consequences: hashability and dict keys, functions changing the caller argument, the mutable default argument bug, thread-safety and caching benefits of immutables.

Code / implementation expected: Optional — a short snippet showing aliasing and += on a list vs a tuple is a strong addition.

mutability

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Solution

Target Audience: Beginners preparing for Python interviews — no prior Python knowledge assumed. Difficulty: Easy Python version: Behaviour checked on CPython 3.11 (python3.11 and the in-page Pyodide 0.25.1 runtime, which is CPython 3.11.3).

How to read this doc: Each idea is explained in plain language first, then tagged with 📌 Interview term: so you also learn the exact word an interviewer expects. Every output shown in the Verified section was copied from a real run of the examples on this page.

1. Why This Even Matters — A Story First

Picture a shared whiteboard and a printed poster in an office. Anyone can walk up to the whiteboard and add a line; everyone who looks at it afterwards sees that line, including people who never knew someone edited it. The poster cannot be edited at all. If you want a different poster, you print a new one and hang it in pla

The solution is hidden so you can try first

10 min read · 10 sections

Run the code

Edit the code and run it here. Each console line with an expected value becomes a test.

PythonAliasing vs rebinding, and += on a list vs a tuple

Practice

PythonShallow immutability, the tuple += gotcha and function arguments

Practice

Discussion

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