gophernotes

https://github.com/gopherdata/gophernotes I've had this bookmarked for some time and just havent gotten around to it.

> You do know that statically typed languages have REPLs too? Like the ML family, including Haskell. I do, but that I don't see how that relates to the bit of my post which you've quoted. I certainly didn't claim or imply that REPL and static type systems were mutually exclusive, only that REPLs are a poor substitute for many static analysis tasks. > And when using something like a Jupyter notebook with a kernel for your compiled language https://github.com/gopherdata/gophernotes you can do similar interactive programming. Yeah, I'm aware. I operate a large JupyterHub cluster (among many other things) at work. :) > Lisp REPLs take that a step further, as you interact with and in your whole actually running program. That sounds nice, but it's too abstract to persuade IMHO. Personally, it seems like REPLs can make certain feedback loops a b

> People rave about their repl, but it feels like they're comparing it to a script iteration loop rather than static analysis tooling. You do know that statically typed languages have REPLs too? Like the ML family, including Haskell. And when using something like a Jupyter notebook with a kernel for your compiled language https://github.com/gopherdata/gophernotes you can do similar interactive programming. REPLs are about trying out ideas or changes. Lisp REPLs take that a step further, as you interact with and in your whole actually running program.

Why can't you just build libraries to make Go a better language for data science? There's already Go support for a Jupyter Notebooks kernel: https://github.com/gopherdata/gophernotes

There is a Go kernel for jupyter/nteract. https://github.com/gopherdata/gophernotes

others have already mentioned Gonum: - https://gonum.org Gonum is almost on par with _e.g._ NumPy/SciPy (most notably lacking: ODEs). So still ways to Go but it's getting there. Go-HEP is my attempt to bring a few High Energy Physics oriented packages to particle physicists: - https://go-hep.org I've also written a few words on why I think Go is great for science: - https://sbinet.github.io/posts/2018-07-31-go-hep-manifesto/ TL;DR: Go is great b/c it brings great s/w engineering practices and a s/w engineering-friendly environment to scientists. Admittedly, generics will change how packages are written. So some code churn will take place when/if they land, but the Go community learned the lessons from Python2/3 and Perl5/6. Expect a better migration path. Lastly, I guess the 2 remaining weak points of Go are: - runtime performances sub-pa

For information, it's not the only Go kernel for Jupyter. There is also https://github.com/gopherdata/gophernotes which uses a different approach: contains a portable Go interpreter, instead of invoking the Go compiler for each Jupyter cell as lgo does (lgo is currently linux only)

The one that has seemed reasonable last I checked, was gore [1]. Then, you might want to check out the Golang Jupyter kernel as well (gophernotes [2]), which mentions gore as an inspiration. [1] https://github.com/motemen/gore [2] https://github.com/gopherdata/gophernotes

FWIW: You can use Go in Jupyter https://github.com/gopherds/gophernotes

Interactivity can be an important feature for scientific programming, but so is having correct underlying algorithms that are implemented in a clean, composable way. Go is a good language for such implementations. > Go doesn't have a nice REPL like IPython, and it never will. https://github.com/gopherds/gophernotes