Python 3 Deep Dive Part 4 Oop High Quality

Quality OOP avoids "naked" attributes when logic is required.

Welcome to of our Python 3 Deep Dive series. If you’ve made it this far, you already understand control flow, functions, and iterables. Now, it’s time to tackle the paradigm that separates “scripting” from “software engineering”: Object-Oriented Programming (OOP) .

@radius.setter def radius(self, value): if value < 0: raise ValueError("Radius cannot be negative") self._radius = value

rp = RegularPoint(); rp.x, rp.y = 1, 2 sp = SlottedPoint(); sp.x, sp.y = 1, 2 python 3 deep dive part 4 oop high quality

OOP’s greatest power is also its greatest danger: inheritance. High-quality OOP strictly follows the : derived classes must be substitutable for their base classes without altering correctness.

class Database(metaclass=SingletonMeta): pass

When accessing obj.attr , Python follows a strict lookup hierarchy: Quality OOP avoids "naked" attributes when logic is required

def __init__(self, x, y): self.x = x self.y = y

This narrative is structured like a technical chapter in an advanced book, blending conceptual depth with practical, quirky Python examples.

Design patterns solve common structural problems. Python's dynamic nature often simplifies traditional structural patterns. The Singleton Pattern (Via Metaclasses) Now, it’s time to tackle the paradigm that

: Class attributes are shared; instance attributes are local to each object.

: Favor composition over inheritance or use an abstract base class ( Shape ).