I did not mean output as in the actual result of running the code. I mean looking at two pieces of code that functionally may do the same thing but the code its

I did not mean output as in the actual result of running the code. I mean looking at two pieces of code that functionally may do the same thing but the code itself is written in a completely different style. One of the other differences I have seen, but didn't specifically mention, is unit tests themselves. The web app probably has a ton of them. The Jupyter notebook probably does not. Which is fine, the language is being used for different purposes. But an advantage of having those types in place is that you can do the static analysis to ensure everything is being used correctly within your codebase. And then you can have your unit tests concentrate on the actual usage rather than other stuff. There is no need to check for / test with a null value when you already know that function is never being passed a null value. And adding those hints has helped me many times with catching edge cases before they even happen where the existing unit tests were never going to hit that edge case.