[wodenokoto]

The argument is if you need to do something slightly different, but straight forward (and this doesn't even have to be in you algorithm, it could be in your data prep) you either have to accept a slow loop or go hunt the documentation and come up with some obscure API or some very clever combination of API's.

To my knowledge if you stick to things that calls optimized C and fortran code, it's a draw between the compiled code and Julia.

But even boring problems ends up doing things that are easily expressed in a loop, but ends up being a hard to read chain of pandas.

[datanecdote]

In the uncommon event I need to write a loop from scratch, and I need it to be really fast, I just rewrite that one jupyter cell in cython or numba. But that is a small piece of my codebase.

I agree that Julia code is aesthetically superior to a long chain of Pandas code. But at this point I’m used to reading a bunch of chained pandas code. Often I think of myself as more of a Pandas programmer than a Python programmer.