The wealthy merchants of Pythoria loved their privacy. They didn't want just anyone touching their gold. They used .
It is a static method that takes the class ( cls ) as its first argument.
d = D() d.process() # Output: # D process # B process # C process # Note: A.process() is NOT called here because C did not call super(). # If C called super(), A would run.
Descriptors are the secret sauce behind property , classmethod , and staticmethod . A descriptor is an object attribute with "binding behavior," one whose attribute access is overridden by methods in the descriptor protocol: __get__ , __set__ , and __delete__ .
This deep dive explores the advanced mechanics of Python 3 OOP, focusing on attribute resolution, memory optimization, and the inner workings of the object lifecycle. 1. The Dynamic Class: Type and Creation python 3 deep dive part 4 oop
Python supports multiple inheritance, which brings up a classical architectural challenge: resolving method collisions (the Diamond Problem). Python cleanly handles this using the algorithm to compute the Method Resolution Order (MRO) . Understanding MRO Lookup
class OptimizedNode: __slots__ = ['x', 'y'] def __init__(self, x, y): self.x = x self.y = y Use code with caution.
class Multiplier: def __init__(self, factor): self.factor = factor
In Python 3, super() is often called without arguments. It is actually a proxy object that delegates method calls to the next class in the MRO. The wealthy merchants of Pythoria loved their privacy
Advanced OOP in Python is about understanding the that underpins the language. Descriptors explain how properties work. Metaclasses explain how classes work. The MRO explains multiple inheritance.
Sized.register(MySequence)
Restricts instance attributes to an explicit list, swapping out __dict__ for compact optimization. Memory Optimization with __slots__
def __init__(self, x, y): self.x = x self.y = y It is a static method that takes the
While introductory courses teach the syntax of classes and objects, a "deep dive" requires understanding the underlying machinery: how attributes are stored, how method resolution works, how data encapsulation is actually enforced (or not), and the architectural patterns enabled by Python's dynamic nature.
The course by Fred Baptiste is an advanced exploration of Object-Oriented Programming that moves beyond basic syntax to examine how Python’s object model operates under the hood. It is specifically designed for experienced developers who already possess a strong grasp of functional Python, including closures, decorators, and generators. Core Pillars of the Deep Dive
: Understanding the relationship between class objects and instance objects, including class-level data and function attributes. Methods and Binding
@temperature.setter def temperature(self, value): if value < -273.15: raise ValueError("Temperature below absolute zero is impossible") self._temperature = value
class Shape(ABC): @abstractmethod def area(self): pass