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!
If anybody is reading this and would like to submit a talk on it or Haskell itself to the Carolina Code Conference, please do so. Our call for speakers is open until the end of March and I've been hoping to get a Haskell talk in for the last couple of years.
> There is now a DataFrame.Typed API that tracks the entire schema of the dataframe - column names, misapplied operations etc are now compile time failures and you can easily move between exploratory and pipeline work.
This makes complex dashboards so much easier to build, because in Python you have to test everything in the dashboard to make sure a change to a common dataset didn’t break anything.
Is there a good web dashboard library like streamlit for Haskell I wonder?
This looks so cool, just put it on top of my todo list. My Haskell skills are mediocre but I love the language. I get by with a subset of the language.
Strong typing and data science seems like a good combination.
In my experience it's tough to sell to some scientists (they like to work with R and Python here), but when it's tied with pipelines that ultimately publish materials (rather than every step of the way), it's extremely helpful and streamlines the QA process of ensuring correct data is packaged with publications.
When I've audited some of the published data at our org, there are errors that would have been caught with even basic type-safety. That's how I got the green light to start harassing my team with type safety in our pipelines.
Of course, as with all things in programming, it isn't a silver bullet. It adds a layer of rigor that can slow things down, and there are often (seemingly always) nuances which can't be caught easily by most type systems. Things like complex relations between values (like 'if in Y is in [range], X must be null, and Z must be one of [a, b, c]'). Even so, eliminating categories of errors is worthwhile, and makes it easier to focus on the more complex challenges.
Over all I'd agree though, it's a good combination.
I feel like I've been waiting for this to mature for a decade. I love that the vision has been realized despite the enthusiasm for functional programming languages cooling off somewhat.
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[ 4.3 ms ] story [ 37.2 ms ] threadDataHaskell in general is revived and improving on multiple fronts. Exciting stuff!
https://blog.carolina.codes/p/call-for-speakers-2026-is-open
This makes complex dashboards so much easier to build, because in Python you have to test everything in the dashboard to make sure a change to a common dataset didn’t break anything.
Is there a good web dashboard library like streamlit for Haskell I wonder?
You can try it from https://www.datahaskell.org/ under "try out our current stack"
Strong typing and data science seems like a good combination.
When I've audited some of the published data at our org, there are errors that would have been caught with even basic type-safety. That's how I got the green light to start harassing my team with type safety in our pipelines.
Of course, as with all things in programming, it isn't a silver bullet. It adds a layer of rigor that can slow things down, and there are often (seemingly always) nuances which can't be caught easily by most type systems. Things like complex relations between values (like 'if in Y is in [range], X must be null, and Z must be one of [a, b, c]'). Even so, eliminating categories of errors is worthwhile, and makes it easier to focus on the more complex challenges.
Over all I'd agree though, it's a good combination.
Now hoping to build a bunch of Neuro symbolic AI on top of this.