Super with Inheritance from Built-in Types in Python: A Deep Dive
on channel: vlogize
Explore the intricacies of using `super` in Python with built-in types. Learn effective strategies to handle inheritance with custom types and resolve common issues.
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This video is based on the question https://stackoverflow.com/q/69105023/ asked by the user 'Daniel Konopka' ( https://stackoverflow.com/u/15285278/ ) and on the answer https://stackoverflow.com/a/69105268/ provided by the user 'yuri kilochek' ( https://stackoverflow.com/u/1554020/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.
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Understanding the Problem: Using super with Built-in Types
When working with custom types in Python that mimic built-in types, developers often face challenges related to inheritance and initialization. A common case arises when creating classes that inherit from types like int or list. In this post, we will explore a specific scenario that illustrates this problem and how to elegantly resolve it.
The Code Example
Imagine you are trying to create a custom class Value that behaves like built-in types while storing additional information about the value it holds. Here’s a simplified version of your code:
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This works correctly since the custom class behaves just like an int, allowing you to access additional info and methods from Value. However, issues arise when you switch from int to list:
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Investigating the Issue
The problem you are encountering is that the list class requires its values to be passed differently compared to immutable types like int or str. The root of the issue lies in how these types handle initialization:
Immutable Types: Classes like int and str don’t have their own _init_ method. They inherit from object and are fully initialized in the _new_ method.
Mutable Types: Classes like list have their own _init_ method that expects values passed in a specific way.
A Workaround: Special Case for Lists
You implemented a workaround by checking if the input value is a list:
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While this workaround does address the issue, it’s not the most elegant solution since it necessitates special case handling for each mutable type.
A More Elegant Solution
To create a more generic solution, we can check if the _init_ method of the base class is different from the default _init_ in object. This way, we only call super().__init__() when necessary:
[[See Video to Reveal this Text or Code Snippet]]
Benefits of This Approach
Simplicity: You avoid multiple conditional checks for each specific type.
Flexibility: The code remains adaptable for additional types, simplifying future maintenance and extension.
Clarity: This solution clarifies intent, indicating that super().__init__() is based solely on the type's behavior rather than its nature (mutable vs immutable).
Conclusion
Understanding the intricacies of inheritance and initialization with built-in types in Python can be challenging. By recognizing how different types manage their initialization, you can avoid common pitfalls when using super(). This allows you to create robust and elegant custom types that effectively extend Python’s native functionality.
In this post, we’ve explored an effective way to manage inheritance with built-in types, ultimately leading to clearer, more maintainable code.
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