What is the @property decorator?

Q: What is the @property decorator?

**Target Audience:** Python beginners who know classes, up to candidates expected to explain descriptors **Difficulty:** Medium **Python version:** behaviour checked on CPython 3.11 (python3.11 and the in-page Pyodide 0.25.1 runtime); error messages also checked on 3.12 and 3.13 > **How to read this doc:** Every idea is first explained in plain words. When a term has a precise name an interviewer expects to hear, it is introduced as a 📌 **Interview term** callout. The Quick Glossary at the end lists all of them. ## 1. Why This Even Matters — A Story First A thermostat on the wall shows "21 °C". You read it like a number. You can also turn it to 25, as if writing a number. But behind the display there is logic: the thermostat refuses 90 °C, it converts to Fahrenheit for an American visitor, it may check a sensor every time you look. You never call `thermostat.get_temperature()`; you just read the dial. `@property` gives a class that kind of dial. Callers write `circle.radius` and `

The problem

Question presented to candidate: "In Java you would write getRadius() and setRadius(). In Python people write @property. What does it do, how do you add a setter, and what is actually happening when I read circle.radius?"

What a strong answer should cover:

@property turns a method into a managed attribute: reading obj.area calls the getter, no parentheses; @area.setter and @area.deleter handle assignment and del.
Why it exists: start with a plain public attribute and later add validation or computation without changing any calling code, so no Java-style getters are needed up front.
A property without a setter is read-only; assigning raises AttributeError (on 3.11: "property 'area' of 'Circle' object has no setter").
Under the hood: property is a class implementing the descriptor protocol; it is a data descriptor, so it wins over the instance __dict__ in attribute lookup.
.setter returns a new property object, which is why the setter function must reuse the same name; store the value under a different name such as _radius.
Trade-offs: keep getters cheap and side-effect free; for an expensive value computed once, functools.cached_property (a non-data descriptor that stores into __dict__).

Code / implementation expected: Yes — a class with a validated property (getter plus setter) and a read-only computed property.

property

Try it yourself

Sketch your approach before you read the solution. That is what the interview tests.

Stuck? Get a hint.

A short nudge toward the approach. It will not give the answer away.

Solution

Target Audience: Python beginners who know classes, up to candidates expected to explain descriptors Difficulty: Medium Python version: behaviour checked on CPython 3.11 (python3.11 and the in-page Pyodide 0.25.1 runtime); error messages also checked on 3.12 and 3.13

How to read this doc: Every idea is first explained in plain words. When a term has a precise name an interviewer expects to hear, it is introduced as a 📌 Interview term callout. The Quick Glossary at the end lists all of them.

1. Why This Even Matters — A Story First

A thermostat on the wall shows "21 °C". You read it like a number. You can also turn it to 25, as if writing a number. But behind the display there is logic: the thermostat refuses 90 °C, it converts to Fahrenheit for an American visitor, it may check a sensor every time you look. You never call thermostat.get_temperature(); you j

The solution is hidden so you can try first

5 min read · 10 sections

Run the code

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

PythonGetter, validating setter, deleter and a read-only computed property

Practice

PythonUnder the hood: the descriptor, setter copies, property() and cached_property