[srer]

We could build such libraries, and people have built some.

However the task at it's heart is a vast duplication of work, and while Go has a lot of things going for it, it doesn't seem enough to sway many data scientists into reinventing their wheels in Go.

I don't blame them. Rewrites being difficult to justify or motivate when you already have a compelling implementation is part of the reason why we have significant amounts of FORTRAN77 code still kicking around today. It is also why for many things we opt to just write wrappers around existing C libraries to call them from other languages.

It has many shortcomings, but overall I prefer the sharing of a library across languages, each with it's own bindings that can attempt to make it more idiomatic to that specific language. The Go culture/community doesn't favor this approach, the Python community embraces it.