Hacker News: mchav

New comment by mchav in "Pandas feels clunky coming from R. What about Haskell?"

mchav — Wed, 22 Apr 2026 13:54:31 +0000

I think the original author picked this example to broadly illustrate how easy it is to make ad hoc changes to your query without worrying about lot about implementation details. Polars, for example, converges on a similar API and gives you the flexibility. You can iterate then refactor easily later to what you consider good practice.

Pandas feels clunky coming from R. What about Haskell?

mchav — Wed, 22 Apr 2026 04:51:51 +0000

Article URL: https://mchav.github.io/being-less-clunky/

Comments URL: https://news.ycombinator.com/item?id=47859169

Points: 26

# Comments: 7

Interpretable models with boosting, symbolic regression and e-graphs

mchav — Thu, 09 Apr 2026 07:51:25 +0000

Article URL: https://mchav.github.io/grow-and-mow/

Comments URL: https://news.ycombinator.com/item?id=47700514

Points: 1

# Comments: 0

What category theory teaches us about dataframes

mchav — Sun, 29 Mar 2026 08:44:25 +0000

Article URL: https://mchav.github.io/what-category-theory-teaches-us-about-dataframes/

Comments URL: https://news.ycombinator.com/item?id=47561426

Points: 190

# Comments: 65

New comment by mchav in "Dataframe 1.0.0.0"

mchav — Tue, 24 Mar 2026 05:10:45 +0000

Had always hoped for something like this since the days of Spark and Frameless. Better late than never.

Now hoping to build a bunch of Neuro symbolic AI on top of this.

New comment by mchav in "Dataframe 1.0.0.0"

mchav — Mon, 23 Mar 2026 22:53:01 +0000

No but something is in the works! We are building reactive notebooks that we will eventually give export capabilties.

You can try it from https://www.datahaskell.org/ under "try out our current stack"

New comment by mchav in "Dataframe 1.0.0.0"

mchav — Mon, 23 Mar 2026 14:24:04 +0000

Author here: Would have loved to but this is round about my wedding anniversary. Will ask some Haskell friends to submit though.

New comment by mchav in "Dataframe 1.0.0.0"

mchav — Mon, 23 Mar 2026 14:20:22 +0000

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 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.

Show HN: Sabela – A Reactive Notebook for Haskell

mchav — Mon, 02 Mar 2026 04:50:12 +0000

Article URL: https://www.datahaskell.org/blog/2026/03/01/sabela-reactive-haskell-notebooks.html

Comments URL: https://news.ycombinator.com/item?id=47214014

Points: 3

# Comments: 0

Show HN: Using an LLM as a "semantic regularizer" for feature engineering

mchav — Sat, 17 Jan 2026 15:45:37 +0000

Article URL: https://medium.com/@mschavinda/pruning-over-engineered-features-with-help-from-an-llm-90e73e4f22ee

Comments URL: https://news.ycombinator.com/item?id=46658934

Points: 1

# Comments: 0

Learning better decision trees – LLMs as Heuristics for Program Synthesis

mchav — Sat, 17 Jan 2026 05:58:14 +0000

Article URL: https://mchav.github.io/learning-better-decision-tree-splits/

Comments URL: https://news.ycombinator.com/item?id=46655701

Points: 1

# Comments: 0

State of Haskell Survey 2025

mchav — Fri, 12 Dec 2025 07:01:38 +0000

Article URL: https://www.surveymonkey.com/r/6M3Z6NV

Comments URL: https://news.ycombinator.com/item?id=46241575

Points: 3

# Comments: 0

Haskell IS a great language for data science

mchav — Fri, 05 Dec 2025 07:33:02 +0000

Article URL: https://jcarroll.com.au/2025/12/05/haskell-is-a-great-language-for-data-science/

Comments URL: https://news.ycombinator.com/item?id=46157705

Points: 6

# Comments: 0

New comment by mchav in "Comparing xeus-Haskell and ihaskell kernels"

mchav — Sat, 29 Nov 2025 19:46:38 +0000

RE Jupyter not having advanced features.

Yeah it's a bummer. It seems that notebooks that support these sort of "reactive" workflows are custom built around that model. Marimo, Pluto.jl, and observable are mostly language specific. Creating one would be non trivial.

Do you have your approach documented (tutorial style) anywhere?

New comment by mchav in "Comparing xeus-Haskell and ihaskell kernels"

mchav — Sat, 29 Nov 2025 19:43:45 +0000

The rule of thumb is somewhere between 5 and 10x difference. Which is large if you're going to do anything heavy but for most practical purposes it's fine. Roughly the difference between C and Python.

Comparing xeus-Haskell and ihaskell kernels

mchav — Wed, 26 Nov 2025 06:13:26 +0000

Article URL: https://www.datahaskell.org/blog/2025/11/25/a-tale-of-two-kernels.html

Comments URL: https://news.ycombinator.com/item?id=46054661

Points: 13

# Comments: 8

Welcome to DataHaskell

mchav — Tue, 11 Nov 2025 22:59:22 +0000

Article URL: https://www.datahaskell.org/blog/2025/11/11/welcome-to-datahaskell.html

Comments URL: https://news.ycombinator.com/item?id=45894069

Points: 6

# Comments: 2