Скачать или смотреть Creating a Decorator to Transform Object States in Python

Learn how to leverage decorators in Python to dynamically store methods in an object's attribute for effective object-oriented programming.
---
This video is based on the question https://stackoverflow.com/q/63690380/ asked by the user 'mccandar' ( https://stackoverflow.com/u/7184515/ ) and on the answer https://stackoverflow.com/a/63691037/ provided by the user 'acushner' ( https://stackoverflow.com/u/129600/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Decorator to change the state of an object

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding Python Decorators for Class Methods

Creating scalable and maintainable code is a common challenge faced by developers, especially when it comes to object-oriented programming (OOP). A recent dilemma presents a need to use decorators efficiently while inheriting classes, with the goal of storing specific methods in an attribute for further processing. This article addresses how to implement a decorator that changes the state of an object in a class hierarchy effectively.

The Problem: Using Decorators to Store Methods

When attempting to build a class hierarchy with dynamic methods, a developer wants to use a decorator to append certain methods to an attribute called methods. For example, applying methods with the @ filtermethod decorator should sequentially filter a list of numbers. However, upon testing, they find that the methods are not being applied, resulting in an unexpected output.

Here’s the original code snippet that illustrates the challenge:

[[See Video to Reveal this Text or Code Snippet]]

In this case, the output did not reflect the expected filtered list because the methods were not properly stored and executed. Let's explore how we can adjust this approach.

The Solution: Employing _init_subclass_ for Method Storage

To resolve this, we need to make modifications that ensure the decorator correctly identifies and stores methods to be processed. The key lies in utilizing the _init_subclass_ magic method that allows us to gather decorated functions at the class level.

Updated Decorator

Here’s how we adjust the decorator to tag our filter methods without invoking them directly:

[[See Video to Reveal this Text or Code Snippet]]

Revised Class Structure

Next, we adapt the parent class to collect all methods marked by our decorator during subclass initialization:

[[See Video to Reveal this Text or Code Snippet]]

Example Implementation

Now, we can define our subclasses as before, while benefiting from the changes made:

[[See Video to Reveal this Text or Code Snippet]]

Final Test

After implementing these modifications, calling the apply_filter method should now yield the desired result. For instance:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In this post, we have explored the challenges of using decorators in Python classes, particularly for method storage and dynamic execution. By leveraging _init_subclass_ along with tailored decorators, we navigate through complexity and achieve an elegant solution to transforming the state of objects based on filtered methods.

Whether you're a seasoned developer or new to Python, understanding these patterns can significantly enhance your programming toolkit for creating dynamic and efficient applications.