Python 3 Deep Dive Part 4 Oop High Quality [2021] -

class Bird: def (self, mover, flyer): self.mover = mover self.flyer = flyer def move(self): return self.mover.move() def fly(self): return self.flyer.fly()

Use when you need to optimize memory for millions of small object instances.

Part 1: Mainly functional programming. Part 2: Mainly iterables, iterators and generators. Part 3: Mainly hash maps. Part 4: OOP. Python 3: Deep Dive (Part 4 - OOP) - Udemy python 3 deep dive part 4 oop high quality

Descriptors are the backbone of Python’s advanced attribute lookup. They power features like property , classmethod , and staticmethod . A descriptor is simply an object that implements at least one method in the descriptor protocol: __get__ , __set__ , or __delete__ . Non-Data vs. Data Descriptors

By default, Python instances store attributes in a __dict__ (dictionary). This is flexible but memory-heavy. __slots__ tells Python to use a fixed-size array. class Bird: def (self, mover, flyer): self

Coverage of slots , single inheritance, enumerations, and custom exception handling.

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def my_meta(name, bases, dct): dct['version'] = 1.0 return type(name, bases, dct)

class A: def show(self): print("A") class B(A): def show(self): print("B") super().show() # Works, but rigid Part 3: Mainly hash maps

Mastering advanced object-oriented programming in Python is a journey from writing code that works to writing code that is a joy to maintain, extend, and share. By understanding and applying the concepts in this guide—from the descriptor protocol and metaclasses to SOLID principles and design patterns—you are equipping yourself to build systems that are not only functional but also elegant and resilient.