Python 3 Deep Dive Part 4 Oop High Quality

class A: def __init__(self): print("A init") super().__init__() # Essential for MRO flow

Use property for single attribute validation. Use descriptor for reusable logic across many attributes.

Instead of implementing > or < explicitly, you can use dunder methods to enable operators like == , < , > : __eq__(self, other) : == __lt__(self, other) : < Emulating Container Types python 3 deep dive part 4 oop high quality

class Point: __slots__ = ('x', 'y') def __init__(self, x, y): self.x = x self.y = y

class A(Base): def work(self): print("A.work start") super().work() print("A.work end") class A: def __init__(self): print("A init") super()

class Circle: def __init__(self, radius): self._radius = radius @property def radius(self): """Getter for radius.""" return self._radius @radius.setter def radius(self, value): """Setter with data validation.""" if value < 0: raise ValueError("Radius cannot be negative") self._radius = value Use code with caution. Dynamic Attribute Access: __getattr__ vs __getattribute__

class Circle: # No explicit inheritance def draw(self, canvas): print(f"Circle on canvas") : Triggers name mangling

__str__ provides a readable string for end-users, while __repr__ must return an unambiguous string representation, ideally matching the expression needed to recreate the object.

: Triggers name mangling . The interpreter renames the attribute to _ClassName__attribute to prevent accidental overriding in subclasses.

Python handles Object-Oriented Programming (OOP) differently than rigid languages like Java or C++. It relies on a philosophy of dynamic lookup, open inspection, and runtime customization. Writing high-quality, enterprise-grade Python requires moving past simple classes and inheritance into the underlying mechanisms of the language.