Python supports AOP by letting you dynamically modify its classes at runtime (which in Python is typically called monkeypatching rather than AOP). Here are some of my AOP use cases:
I have a website in which every page is generated by a Python function. I'd like to take a class and make all of the webpages generated by that class password-protected. AOP comes to the rescue; before each function is called, I do the appropriate session checking and redirect if necessary.
I'd like to do some logging and profiling on a bunch of functions in my program during its actual usage. AOP lets me calculate timing and print data to log files without actually modifying any of these functions.
I have a module or class full of non-thread-safe functions and I find myself using it in some multi-threaded code. Some AOP adds locking around these function calls without having to go into the library and change anything.
This kind of thing doesn't come up very often, but whenever it does, monkeypatching is VERY useful. Python also has decorators which implement the Decorator design pattern (http://en.wikipedia.org/wiki/Decorator_pattern) to accomplish similar things.
Note that dynamically modifying classes can also let you work around bugs or add features to a third-party library without actually having to modify that library. I almost never need to do this, but the few times it's come up it's been incredibly useful.
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