[jerf]

I mean this without rancor or insult, but a lot of data scientists may use Python, but are definitely not Python programmers. They know the subset of Python necessary to process data, and literally not one bit more. They would have no idea how to create an iterator function, their own "with" handler, may not even know how to create a new subclass with a method. They just take data in, chew on it, and spit it out.

Again, not an insult intended to them. They have their job and they do it, and I don't know much about their world either, after all. And of course you can find some data scientists who also deeply know Python. My point is merely that modeling them all generically as "Python programmers" in your head can lead to a model that makes bad predictions, which I found in my brief stint in that world can include you building tools for them that expect more out of them than they have.

[hedgehog]

That's not to mention getting dependencies installed. I know a good amount about everything from the silicon up and it can still take some time to get to the point where I have a Python ML environment working. Debugging whichever vendor's barque build process, broken drivers, etc etc, not fun and not something we probably want every notebook user to spend time on.