In the Python ecosystem, "everything is an object." While most developers are comfortable creating a class and instantiating it, a true deep dive into Part 4 of the Python 3 journey requires understanding the machinery beneath the surface: , metaclasses , slots , and the Method Resolution Order (MRO) . 1. The Foundation: Classes as Objects
Python supports multiple inheritance, allowing a class to inherit from more than one parent class. This introduces a structural vulnerability known as the , where a child class inherits from two parent classes that both share a common ancestor. The C3 Linearization Algorithm
class Base: def log(self, msg): pass # terminal method
Inspired by the deep structural breakdowns found in the popular Python 3: Deep Dive (Part 4 - OOP) masterclass, this guide moves past beginner tutorials. We will uncover how Python handles objects under the hood, how functions transform into bound methods, and how to harness advanced mechanisms like descriptors and custom metaclasses. 1. The Class Body: A Living Namespace
class SingletonClass: _instance = None def __new__(cls, *args, **kwargs): if cls._instance is None: # Delegate raw memory allocation to the base object class cls._instance = super().__new__(cls) return cls._instance def __init__(self, value): self.value = value Use code with caution. 3. Properties and the Descriptor Protocol python 3 deep dive part 4 oop
class BankAccount: def __init__(self, balance): self._balance = balance @property def balance(self): return self._balance @balance.setter def balance(self, value): if value < 0: raise ValueError("Balance cannot be negative") self._balance = value Use code with caution. 5. Inheritance and Polymorphism
Python 3: Deep Dive (Part 4 - OOP) course, created by Fred Baptiste
Welcome to Part 4 of the Python 3 Deep Dive series, where we'll explore the world of Object-Oriented Programming (OOP) in Python. In this section, we'll cover the fundamental concepts of OOP, including classes, objects, inheritance, polymorphism, and more.
: Transitioning from basic getters and setters to the @property decorator and the descriptor protocol ( __get__ , __set__ ), which provides fine-grained control over attribute access. In the Python ecosystem, "everything is an object
class PreRegistration: def __new__(cls, *args, **kwargs): print("1. Creating the physical object memory space") instance = super().__new__(cls) return instance def __init__(self, name): print("2. Initializing attributes on the created object") self.name = name Use code with caution. Key Differences
class LogPlugin(Plugin): def run(self): print("Logging...")
Lina discovered Python's data model. Implementing , iter , and context managers made her Catalog friendlier.
By default, Python uses a dynamic dictionary ( __dict__ ) to store instance attributes. This allows you to add new attributes to an object on the fly, but it consumes a significant amount of RAM. This introduces a structural vulnerability known as the
cls.__init__ = new_init return cls
In Python, everything is an object – integers, strings, functions, classes, and even types themselves. Each object has:
I can explain the benefits of using __slots__ or walk through a practical example of a metaclass for tracking class instantiation. Python 3: Deep Dive (Part 4 - OOP) - Udemy
When you instantiate a class, Python typically creates a __dict__ attribute for the instance. This is a standard Python dictionary mapping attribute names to values.
class Catalog: def __init__(self): self._items = {}