Eh, not really in the data wrangling and machine learning space, at least not quite yet. It's not really amenable to writing libraries for numerical processing - it ends up being super tedious to write something like numpy (you have to implement for every data type, there's at least a dozen+) and nigh-impossible to write an API like pandas, because dataframes are just too dynamic. You could do these things, but it's just super unpleasant.

I'm hoping generics makes it easier to write things like numpy, without doing `meao_u8`, `mean_u16` etc.

+ there's bool, int/uint8,16,32,64, float32/64, complex64/128, that's 13 right there, plus object, a bunch of extended sizes on some architectures, and c-alias types.

That's a fair point - I don't work in the analysis or ml spave so have no understanding of it!

Other people point out ML is a use case where Go isn't suitable. And I agree.

But as someone who used to write a LOT of Python, Go has replaced it for anything service or business-logic oriented, and while I do very little data wrangling and machine learning, Julia would be my go to for that. Julia and Go are in, many ways, complementary opposites. If you need to use PyTorch/TensorFlow, okay, fine Python (I'm not going to seriously advocate for just wrapping it all in PyCall.jl unless it's just incidental usage), but Julia is a really strong language for this stuff nowadays.