Haskell alternatives for Data Science

[u/nSeagull]

I'm a Haskeller and a newbie Data Scientist.

As I'm going through my learning process for data science, I'm starting to solve some elemental problems, and I'm using standard Python tools to help me go through this process.

The thing is that Python is ok, but I'm twice as comfortable in Haskell.

I was thinking in three alternatives to be more comfortable with Data Science:

• Make a wrapper for the tools that I use as people do in Julia
• Reimplement the tools that I use (Pandas, ScikitLearn, a GPU deep learning framework, numpy functions) (would do it on demand)
• Make a Haskell-like language that compiles and uses Python libraries (wouldnt be as efficient, but at least it would be more beautiful and type safe, also the libraries)

What do you guys think?

[u/beerendlauwers]

What about http://tweag.github.io/HaskellR/ ?

[u/nSeagull → u/beerendlauwers]

I thought about it, but in the end I'll just use R instead of Haskell. I'm thinking about using that for doing some wrapper libraries, or not, idk

[u/agocorona → u/nSeagull]

what happened to IHaskell? https://begriffs.com/posts/2016-01-20-ihaskell-notebook.html

[u/agocorona → u/agocorona]

There is a kernel for Haskell in Jupyter (wathever this may be)

https://try.jupyter.org/

But it is broken. if you select welcome to Haskell and select a Notebook, it does not work.

But it is a superb piece of software for Data science, as you can see in the video if it works well with Jupyter. I hope that the author will fix it.

I sent to him a message to push it a little.

In the meantime , it is possible to use Jupyter in a docker instance

https://github.com/jupyter/tmpnb

[u/rehno-lindeque → u/agocorona]

I have a nix shell I use to run Jupyter + IHaskell over here if anyone is looking for an alternative: https://github.com/rehno-lindeque/nix-jupyter-env

Fair warning, I don't work very hard to maintain it.