And there's also a great and growing set of tools for numerical and data related computation.
Scratch that, the current ongoing dev and research work that is making its way onto the haskell hackage is both astonishing and often superlative code. The libs released in the past month and the ones I know will be made available over the coming months... It's just great work!
(I'm biased owing To using some of these tools for fun and profit :-). )
That aside, Pandas is a really nice piece of engineering that really works. It's a good role model for other libs, even when restricted to just its data frame part
Newest ones are: meta-par (a work stealing scheduler as a library that can shuttle work btwn CPU,gpu, and distributing across other machines.)
Pending one you can find online: distributed haskell (the production version is on github, and there's a proof of concept package on hackage called remote)
Things that have been around a bit that have ongoin work: repa, dph, accelerate, and to some extent hmatrix.
There's a few other projects that aren't quite public yet going on that should make it possible to do some of the standard things you might want like a nice staticly typed fast data frame (which is going to look at pandas as an initial role model) and some other parts of the data analysis flow.
and on the DataFrame front, look to https://github.com/cartazio/HasCol in a few days - week for a prealpha/alpha look at what shall be the kernel of the haskell story for doing a nice data frame. :)
Scratch that, the current ongoing dev and research work that is making its way onto the haskell hackage is both astonishing and often superlative code. The libs released in the past month and the ones I know will be made available over the coming months... It's just great work! (I'm biased owing To using some of these tools for fun and profit :-). )
That aside, Pandas is a really nice piece of engineering that really works. It's a good role model for other libs, even when restricted to just its data frame part