Comment by mugul

2 days ago

Looks like a pretty cool community building great tools with care. Using a functional language for data transforms sounds like a sane idea, haven't played around with it yet but it's definitely on my list now.

However they claim using Haskell for data science is "fast", which doesn't really mean anything until you have numbers to show. A little benchmark with pandas and polars wouldn't hurt I guess.

> Using a functional language for data transforms sounds like a sane idea, haven't played around with it yet but it's definitely on my list now.

It's been one of the plays that the F# community has also been trying to make (somewhat with a modicum of help from Microsoft's marketing arm, but not enough help from what I've seen) pitching F# as a language close enough to Python to feel familiar and useful to data science but with the performance help of rich ML types and the modern .NET performance ecosystem.

(To my experience: getting "fast" compared to Python seems easy for most functional languages. Getting data science out of Python seems hard for a lot of sociology reasons more than technical ones.)

Haskell tends to be C-fast.

  • That's not what I see reported, they say Haskell tends to have bad memory layout generally and takes a 5x or so hit to performance.

  • In many cases it's possible to write Haskell that runs as fast as a given C implementation. However the result will usually not be idiomatic Haskell.