4 ms·

I learned some haskell as my hobby language a few years back. It was very cool and forced me to think about programming differently (and finally grok recursion)

I learned some haskell as my hobby language a few years back. It was very cool and forced me to think about programming differently (and finally grok recursion). It never felt like a good language for data analysis to me though (maybe that's cause this library wasn't around? lol). This isn't meant a criticism of this library, instead, I'm curious the use cases the author, if you're around or a user, has in mind. Congrats on the v1 release!

mchav 7mo ago
Author here. At the time I worked in fraud detection and we needed to automate file generation for our BRMS. Initially created this to experiment with “models as dataframe expressions” and Haskell is great for DSL-like stuff. That work is still on going: https://github.com/DataHaskell/symbolic-regression https://github.com/DataHaskell/symbolic-regression and dataframe has a native sparse oblique tree implementation. As it’s grown it’s been pretty cool to have transparent schema transformations instead of every function mapping a statement a dataframe you can have function signatures like: ``` extract :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int, Column "comments" T.Text] -> TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int] -- body of extract transform :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int] -> TypedDataFrame [Column "price" Double, Column "quantity" Int] -- body of transform clean :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int, Column "comments" T.Text] -> TypedDataFrame [Column "price" Double, Column "quantity" Int] clean = transform . extract ``` But you can also do the simple thing too and only worry about type safety if you prefer: ``` df |> D.filterWhere (country_code .==. "JPN") |> D.select [F.name name] |> D.take 5 ``` Being able to work across that whole spectrum of type safety is pretty great.

mchav 7mo ago

Author here. At the time I worked in fraud detection and we needed to automate file generation for our BRMS. Initially created this to experiment with “models as dataframe expressions” and Haskell is great for DSL-like stuff. That work is still on going: https://github.com/DataHaskell/symbolic-regression https://github.com/DataHaskell/symbolic-regression and dataframe has a native sparse oblique tree implementation. As it’s grown it’s been pretty cool to have transparent schema transformations instead of every function mapping a statement a dataframe you can have function signatures like: ``` extract :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int, Column "comments" T.Text] -> TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int] -- body of extract transform :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int] -> TypedDataFrame [Column "price" Double, Column "quantity" Int] -- body of transform clean :: TypedDataFrame [Column "price" (Maybe Double), Column "quantity" Int, Column "comments" T.Text] -> TypedDataFrame [Column "price" Double, Column "quantity" Int] clean = transform . extract ``` But you can also do the simple thing too and only worry about type safety if you prefer: ``` df |> D.filterWhere (country_code .==. "JPN") |> D.select [F.name name] |> D.take 5 ``` Being able to work across that whole spectrum of type safety is pretty great.