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Decorator Patterns: @property, @classmethod, functools.wraps — Expert Verified Classification

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Python's built-in decorators — @property, @classmethod, @staticmethod — are implemented through the descriptor protocol, not magic. Understanding that protocol explains why decorators work, when they fail, and how to compose them correctly.

The descriptor protocol

A descriptor is any object that defines __get__, __set__, or __delete__. When you access an attribute on a class instance, Python's attribute lookup checks whether the class (or any of its bases) defines a descriptor under that name and, if so, calls the descriptor's __get__ method rather than returning the object directly.

@property is a built-in descriptor that turns a method into a managed attribute. Reading the attribute calls __get__ (the getter); assigning calls __set__ (the setter); deleting calls __delete__.

class Temperature:
    def __init__(self):
        self._celsius = 0.0

    @property
    def celsius(self):
        return self._celsius

    @celsius.setter
    def celsius(self, value):
        if value < -273.15:
            raise ValueError("Below absolute zero")
        self._celsius = value

    @property
    def fahrenheit(self):
        return self._celsius * 9/5 + 32

@classmethod and @staticmethod

@classmethod passes the class as the first argument rather than the instance. It is the standard pattern for alternative constructors — methods that create an instance from a different input form.

class Date:
    def __init__(self, year, month, day):
        self.year, self.month, self.day = year, month, day

    @classmethod
    def from_string(cls, date_string):
        year, month, day = map(int, date_string.split('-'))
        return cls(year, month, day)

d = Date.from_string('2024-03-15')

@staticmethod receives neither instance nor class. It is a regular function namespaced inside the class — appropriate for utility functions that are logically related to the class but do not need access to its state.

functools.wraps and the __wrapped__ convention

functools.wraps copies __module__, __name__, __qualname__, __doc__, __dict__, and __annotations__ from the wrapped function to the wrapper. It also sets __wrapped__ to the original function, which allows inspect.unwrap to peel back decorator layers for introspection.

import functools

def trace(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        print(f"→ {func.__qualname__}({args}, {kwargs})")
        result = func(*args, **kwargs)
        print(f"← {result!r}")
        return result
    return wrapper

lru_cache and cache

functools.lru_cache memoises a function's return values keyed on its arguments. Arguments must be hashable. functools.cache (Python 3.9+) is an unbounded version — equivalent to lru_cache(maxsize=None) but faster because it skips the LRU eviction logic.

from functools import cache

@cache
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

print(fibonacci(100))  # fast; calls cached after first invocation