To reach the highest level of Python OOP, you look at how classes themselves are built.
Until then, write Pythonic classes that your future self will thank you for.
class InitializerBase: def __init__(self): pass class LoggingMixin(InitializerBase): def __init__(self): print("Initialization started") super().__init__() class AuditingMixin(InitializerBase): def __init__(self): print("Audit record created") super().__init__() class SecureApplication(LoggingMixin, AuditingMixin): def __init__(self): super().__init__() Use code with caution. 6. Advanced Object Creational Lifecycle
Prevents duplicate base execution in complex inheritance structures. python 3 deep dive part 4 oop high quality
__init__ initializes an already-created instance. __new__ actually creates the instance. It’s a static method (though not decorated as such) that receives the class and returns a new instance.
class BadClass(metaclass=Meta): pass # Raises TypeError: All classes must have a docstring!
class FileStream(Stream): def read(self): return "data" # Forgetting write() raises TypeError on instantiation To reach the highest level of Python OOP,
class Circle: def __init__(self, radius): self._radius = radius @property def radius(self): """Getter for radius.""" return self._radius @radius.setter def radius(self, value): """Setter with data validation.""" if value < 0: raise ValueError("Radius cannot be negative") self._radius = value Use code with caution. Dynamic Attribute Access: __getattr__ vs __getattribute__
class Stream(ABC): @abstractmethod def read(self): pass
s = Secret() print(s._Secret__private) # Accessible via mangled name # print(s.__private) # AttributeError __new__ actually creates the instance
Almost always. Class decorators and __init_subclass__ (Python 3.6+) solve 99% of metaclass use cases more simply.
bundles data and methods within a single unit (a class) and protects internal state from unintended interference.
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