Python 3 Deep Dive Part 4 Oop 【Latest】

Descriptors are reusable attribute logic. They are Python objects that define __get__, __set__, or __delete__. Used for @property, @staticmethod, @classmethod.

class PositiveNumber:
    def __set_name__(self, owner, name):
        self.name = name
def __get__(self, obj, objtype=None):
    return obj.__dict__.get(self.name)
def __set__(self, obj, value):
    if value <= 0:
        raise ValueError(f"self.name must be positive")
    obj.__dict__[self.name] = value

class Order:
quantity = PositiveNumber()
price = PositiveNumber()
def __init__(self, quantity, price):
    self.quantity = quantity
    self.price = price

If you want to learn more about OOP in Python 3, here are some recommended resources:

We hope you found this article helpful! Let us know in the comments if you have any questions or need further clarification on any of the concepts covered.

Diving Deep into Python 3 OOP: A Comprehensive Look at Part 4 Python 3: Deep Dive (Part 4 - OOP)

course, created by Dr. Fred Baptiste, is widely considered one of the most exhaustive resources for mastering Object-Oriented Programming (OOP) in Python. Unlike introductory tutorials, this course focuses on how Python implements OOP under the hood, making it ideal for developers who want to move from "writing code" to "architecting systems". Core Curriculum and Key Topics python 3 deep dive part 4 oop

The course is structured to take you beyond basic class definitions, covering advanced mechanisms and patterns: Classes and Instances

: Deep exploration of class data vs. function attributes and the mechanics of instantiation. Properties and Decorators

: Detailed look at read-only and computed properties, including how to use for lazy loading and caching. Methods and Binding

: Understanding the differences between instance, class, and static methods and how they bind to their respective scopes. Polymorphism and Special Functions

: Mastering the "Pythonic" way of achieving polymorphism through special dunder methods. Advanced Mechanics : Optimizing memory by restricting attribute creation. Descriptors : The underlying protocol behind properties and functions. Metaprogramming : Using metaclasses to customize class creation itself. Enumerations and Exceptions

: Implementing clean data types and robust error handling in an object-oriented way. Learning Experience and Style The course is praised for its rigorous, academic approach: Python 3: Deep Dive (Part 4 - OOP) - Udemy

When the library grew, Lina added a Catalog and LoanManager. Instead of bloating Media with cataloging details, she composed objects — Catalog held indexes, LoanManager tracked due dates. Descriptors are reusable attribute logic

class Catalog:
    def __init__(self):
        self._items = {}
def add(self, item):
        self._items[item.id] = item
def find_by_title(self, title):
        return [i for i in self._items.values() if i.title == title]
class LoanManager:
    def __init__(self):
        self._loans = {}  # item_id -> due_date
def borrow(self, item, days=14):
        if not item.is_available():
            raise RuntimeError("Not available")
        item.check_out()
        self._loans[item.id] = date.today() + timedelta(days=days)
def due_date(self, item):
        return self._loans.get(item.id)

Composition kept responsibilities separated and testable.

Encapsulation is the concept of hiding the implementation details of an object from the outside world and only exposing the necessary information through public methods.

Protocols (from typing) define interfaces via structural subtyping without inheritance.

from typing import Protocol
class Drawable(Protocol):
def draw(self) -> None:
...
class Triangle:
def draw(self) -> None:
print("Triangle")
def render(obj: Drawable) -> None:
obj.draw()
render(Triangle())  # OK – Triangle implements the protocol
 If you want to learn more about OOP

Contrast with ABC: no explicit inheritance, just "looks like a duck".

In Python, everything is an object. An integer, a string, a function, even a class itself—they are all instances of some class. This is the core of Python’s OOP.

from abc import ABC, abstractmethod
class Shape(ABC):
@abstractmethod
def area(self):
pass
@abstractmethod
def perimeter(self):
    pass

class Circle(Shape):
def init(self, radius):
self.radius = radius
def area(self):
    return 3.14 * self.radius ** 2
# Forgetting perimeter() => TypeError on instantiation

Make your objects act like lists or dictionaries.