order = Order() order.quantity = 10 # Works
Mastering Python 3 OOP requires moving beyond basic inheritance and syntax. By exploiting dunder methods, managing attributes via descriptors, leveraging metaclasses for framework-level abstractions, and optimizing performance with slots, you can write Python code that is robust, elegant, and maintainable at scale. Share public link
class Circle(Shape): def __init__(self, radius): self.radius = radius
While Python is not a pure OOP language, it is heavily object-oriented. Every type is a class, and every instance is an object. Understanding this allows you to create robust, maintainable code. Encapsulation and Data Hiding python 3 deep dive part 4 oop high quality
You have now completed a into Python 3’s OOP model. From the object lifecycle and properties to descriptors, slots, and metaclasses, you understand not just how Python OOP works, but when to apply each tool for clean, efficient, and maintainable code.
Python relies heavily on "protocols" rather than explicit interface declarations. Protocols are implemented using special dunder (double underscore) methods. Implementing these makes your custom classes behave like native Python types. The String Representation Protocol
Slots can dramatically reduce memory consumption—by 50% or more—when creating thousands or millions of small objects. However, they come with trade-offs: you lose the ability to add new attributes dynamically, and inheritance with slots requires careful handling. Use slots as an optimization tool, not a default practice. order = Order() order
A property is a special kind of attribute that allows you to execute code when it is accessed, set, or deleted. This is the cornerstone of encapsulation in Python: you can expose an attribute publicly while hiding the logic of validation, computation, or side effects behind getter and setter methods. The @property decorator is the Pythonic way to create managed attributes.
account = BankAccount("1234567890", 1000) print(account.get_balance()) # Output: 1000 account.deposit(500) print(account.get_balance()) # Output: 1500
for strict control over instance creation mechanics and working with immutable types. Every type is a class, and every instance is an object
: __repr__ must return an unambiguous string representation, ideally usable to recreate the object. __str__ provides a readable, user-friendly representation.
Non-data descriptor in the class hierarchy (e.g., a standard method). Class attributes or base class attributes. Fallback to __getattr__ if it exists. Custom Descriptors
print(isinstance(MyClass(), object)) # Output: True print(issubclass(type, object)) # Output: True Use code with caution.