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When attempting to assign attributes to a vanilla object instance like o.attr = 'hello', an AttributeError is raised, indicating that the object lacks the specified attribute. However, this is not an issue for classes inheriting from object, such as Sub, which can assign attributes like s.attr = 'hello'. Why this is the case requires an understanding of Python's internal infrastructure.
To support dynamic attribute assignment, an object must have a __dict__, which serves as a dictionary where arbitrary attributes can be stored. Without a __dict__, there is no storage available for new attributes.
An instance of object does not have a __dict__. Including one in the object definition would result in a circular dependency issue, as dict also inherits from object. Additionally, assigning a __dict__ to every object would introduce unnecessary memory overhead for objects that do not require dynamic attribute assignment.
When a class is created that inherits from object, the situation changes. The creation of a class instance triggers the allocation of a __dict__. This allows subclasses of object to possess dynamic attribute assignment capabilities, but with a memory cost associated with the added flexibility.
Python provides an optimization technique, __slots__, to limit the attributes that can be assigned to an object. By specifying a sequence of string names in the __slots__ attribute, the class instructs Python not to create a __dict__ and instead allocates only the specified attribute slots. This approach reduces memory overhead and can be useful in situations where objects have a fixed set of attributes.
In summary, the inability to assign attributes to object instances stems from the absence of a __dict__, which would introduce memory overhead for objects without dynamic attribute assignment capabilities. Class instances, however, possess a __dict__ and can dynamically assign attributes. The __slots__ attribute provides a memory optimization option by restricting attribute assignment to a fixed set.
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