Because it's a PITA sometimes to use c++. It's easy to accidentally go out of bounds on stuff.
I've arguably got a lot more experience doing scientific programming in c++ and FORTAN, than I do in python. Yet for quick prototyping where performance is almost never an issue, I still elect to use python because it lets me wrap my head around the problem faster. I spend less time fixing stupid errors and thinking about coding and more problem thinking about the problem I really need to solve.
Julia might be even better, I've heard good things, I've just never used it though. In the scientific computing and data science world that I deal with, I'd say at least 95% of the time there's never a need to jump from a hacked together prototype to a more production level product.
My experience with Julia is not deep, but I’ve found it’s really nice to iterate on something that floats towards something that flies in one ecosystem. It’s not harder than Python to get the working implementation up and going - really there is a lower volume of idiosyncrasies.
The only annoying thing I've found is the startup time for the REPL / notebook environments, unless you keep one running all the time. Importing biggish libraries takes some time even if they're already compiled.
I ended up making a custom system image with the libraries I always use (Plots, etc) which does help on that front.
That's a fairly minor gripe, mind you. It's still great.
Some libraries implement i/o formats taht you'd never want to spend time implementing, but are available in Python. Nibable is an exmaple, of neuroimaging formats I never want to understand the details of, but would need to work in Julia.
If language X solves 9 of my 10 problems, and language Y solves the last one of my problems, I can either, A: use language X and call out to language Y to solve all my 10 problems. Or, B: use just language Y to solve 1 of my 10 problems.
