Oop

__add__ and __iadd__ define two distinct behaviors for addition in Python: one creates a new object, the other modifies the existing one. This distinction has real consequences for aliases and shared references, and it catches even experienced developers off guard. add: the addition that creates __add__ is called by the + operator. It must return a new object and leave the operands unchanged. class Vector: def __init__(self, x, y): self.x = x self.y = y def __add__(self, other): return Vector(self.x + other.x, self.y + other.y) a = Vector(1, 2) b = Vector(3, 4) c = a + b # new Vector(4, 6) print(a.x, a.y) # 1 2 — a is unchanged print(id(a) == id(c)) # False — distinct objects a + b calls a.__add__(b). If __add__ is not defined on a or returns NotImplemented, Python tries b.__radd__(a). ...

Accessing r.width and writing r.width = 15 with the same syntax as a plain attribute, while running validation or computation under the hood: that is what @property provides. And when that logic needs to be shared across multiple classes, descriptors come into play. @property: getters and setters without friction @property lets you expose a computed or validated attribute with the same syntax as a regular one. class Rectangle: def __init__(self, width: float, height: float): self._width = width self._height = height @property def width(self) -> float: return self._width @width.setter def width(self, value: float) -> None: if value <= 0: raise ValueError("Width must be positive.") self._width = value @property def area(self) -> float: return self._width * self._height The concrete benefit: the public API does not change. Adding validation to an existing attribute breaks no calling code. @property without a setter creates a read-only attribute. @name.deleter handles deletion via del. ...