Understanding Python Classes, Special Methods, Inheritance, and Polymorphism

The article introduces how to define a Python class, using class Point: def __init__(self, x=0, y=0): self.x = x self.y = y as a basic example, and explains that self is analogous to this in C++/Java.

It shows that special methods (dunder methods) like def __repr__(self): return 'Point({}, {})'.format(self.__x, self.__y) control the string representation shown in the interactive interpreter, while def __str__(self): return '({}, {})'.format(self.__x, self.__y) provides a user‑friendly format.

Operator overloading is demonstrated with def __add__(self, other): return Point(self.__x + other.__x, self.__y + other.__y) , allowing p1 + p2 to produce a new Point instance.

The article then discusses inheritance, presenting an abstract base class class AbstractShape: def area(self): raise NotImplementedError and concrete subclasses class Circle(AbstractShape): def __init__(self, color, r=0.0): super().__init__(color) self.r = r def area(self): return math.pi * self.r * self.r and class Rectangle(AbstractShape): def __init__(self, color, a, b): super().__init__(color) self.a = a self.b = b def area(self): return self.a * self.b .

It highlights that Python lacks built‑in interface enforcement, so abstract classes raise NotImplementedError to simulate abstract methods, and that super() is the recommended way to invoke parent constructors, especially in multiple inheritance scenarios.

Finally, the article notes Python's dynamic nature, allowing methods to be added at runtime (e.g., Circle.area = lambda self: math.pi * self.r * self.r ), and discusses the limitations compared to static languages regarding polymorphism and compile‑time checks.