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I suppose that comes back to use cases. I am one of the 'getting things done crowd', rather than a computer scientist. A lot of my work had been in things like EDI, or similar integration code.

Imagine you are working through a series of EDI files and trying to post them to a badly documented Rest(ish) API of some enterprise system. If the file is bad (for whatever reason) you need to log the exception and put the file in a bad directory.

Pythons use of exceptions for control flow is perfect for this. If file doesn't load for whatever undocumented reason, revert back to log and handle the fallout.

"Oh I see a pattern, this API doesn't like address lines over 40 characters, I will add a custom exception to make the logging clearer, and go and try and see if I can fix this upstream. If not I will have to write some validation"

It is this dirty world of taking some other systems dirty data and posting it to some other systems dirty API that I find Python rules.

I have never worked on a large application where I owned the data end-to-end. Maybe there are better choices than Python for that?

My feelings exactly. I write a lot of kinda scientific code that is not really computational but takes one or more horribly messy datasets and combines them to do some analysis. I now routinely use exceptions to filter out bad/complex data-points to deal with later.

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