In this case, using Numba for the performance-critical aspect was trivially simple, just the addition of a decorator. And I've found that is generally pretty close to true, at least for the kinds of things where I care about performance.
That's why I'm still in the Python world rather than the OCaml world for my algorithmic work. I really like OCaml as a language and would actually prefer to use it, but Python/Numba seems to give me substantially better performance, as well as all of Python's standard libraries.
And Numba lets you turn off the GIL, so you can get multiple cores going at once.
YMMV, of course. I'm not making a general claim that would be true for everyone. But I think it may be worth mentioning that it could be true for more uses than one might assume.
A pure python implementation of python stdlib ready be transpiled could be that bridge.
Also it's possible to support multiple source syntaxes in such an ecosystem. ML based, python based or even a hybrid. In the end all I need is a well documented AST.
Standard ML was where I started. As a language it's great, as an ecosystem it's limited. OCaml seems to be where the action is (and even then its packaging/dependency management isn't great - but then it can't be worse than Python's).
F# has a very good reputation but I haven't used it a lot myself.
I think you object to the name python3, because there is a duck typed interpreter that's popular.
There is demand for iterative creation of software: first get the logic out with the least friction and then think about types, resource leaks, good software engineering practices etc.
pip3 install py2many
py2many --nim=1 foo.py