Python 3 Deep Dive Part 4 Oop -
In this article, we've covered the basics of Object-Oriented Programming (OOP) in Python 3, including classes, objects, inheritance, polymorphism, and encapsulation. We've also provided examples of how to implement these concepts in Python 3.
By mastering OOP concepts, you can write more maintainable, efficient, and scalable software that is easier to understand and modify.
class Rectangle:
def __init__(self, width, height):
self.width = width
self.height = height
def area(self):
return self.width * self.height
class Square(Rectangle):
def __init__(self, side_length):
super().__init__(side_length, side_length)
def area(self):
return self.width ** 2
In the above example, the Square class overrides the area method of the Rectangle class. python 3 deep dive part 4 oop
class Temperature: def __init__(self, celsius): self._celsius = celsius@property def celsius(self): return self._celsius @celsius.setter def celsius(self, value): if value < -273.15: raise ValueError("Below absolute zero") self._celsius = value
Two fundamental pillars rule Python’s object system:
print(isinstance(5, object)) # True
print(isinstance(int, object)) # True
print(isinstance(object, type)) # True (object is an instance of type)
print(isinstance(type, object)) # True (type is an instance of object)
This creates a circular relationship that powers Python’s dynamic nature. In this article, we've covered the basics of
Python’s OOP is distinctive – deeply dynamic, yet expressive and pragmatic. Mastering attribute resolution, descriptors, and metaclasses separates intermediate from advanced Python programmers. However, Pythonic OOP emphasizes simplicity: prefer composition over deep inheritance, protocols over ABCs, dataclasses over manual boilerplate, and only reach for metaclasses when absolutely necessary. The goal is readable, maintainable, and extensible code – not maximal object-orientation.
Protocols are not enforced by the language but expected by the runtime. In the above example, the Square class overrides