icicle VS post-rfc

We used Haskell as a general purpose programming language, so any tool that needed writing we wrote in Haskell (almost). That included AWS infrastructure using Amazonka, cli programs for running data analysis, websites with servant and purescript, new libraries to interact with services like Github and Azure, compilers https://github.com/icicle-lang/icicle. As a general purpose language it is great! The places where we couldn't and didn't use Haskell was either due to needing the JVM or specific JVM integrations like Kafka or Hadoop. Or because the code was written by non-Engineers for things like prototypes or generating reports (usually Python or R) The trouble you run into with Haskell (or any other less popular) language is missing libraries, you need to commit to writing them yourself or changing languages that include them. At one point we needed integration with Azure and MSSQL, and ended up using F# so we had access to .NET. For other Azure services we wrote REST clients to call the endpoints we needed.

Icicle Streaming Query Language

That's what it's best for, but personally I use it for everything. If I ever get into low-level code I'll probably use Rust though.

You can confirm that parsers/tokenizers is ranked "best in class" here though:

https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

Note that this is ported from here: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md which comes with more exposition.

State of the Haskell Ecosystem

According to State of the Haskell ecosystem, Haskell is THE language of choice for implementing compilers, and THE language of choice for writing parsers. Thus, it is not surprising to see more Haskell projects from those particular categories than from other categories.

This is great for understanding what libraries to use in the Haskell ecosystem: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

In particular, I got comfortable reading hackage documentation to understand quickly how to use libraries (aeson, megaparsec, mtl, pipes, etc), got comfortable with the ecosystem (this helped: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md), got comfortable with the main language idioms and features (https://smunix.github.io/dev.stephendiehl.com/hask/tutorial.pdf) and got comfortable with simple things that for some reason had confused me before (case, \case, let).

Gabriella Gonzalez has a great doc that is reasonably up-to-date, sounds similar to what you're looking for? https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

With that being said, Python is without a doubt the best option, and I'd also be very interested to read the articles you found that say that Python is not a good choice because it's been the industry standard for a long time now. Data science and machine learning are one of the areas where the Haskell ecosystem is not as strong as other languages, but libraries and tools do exist. There's a great list of Haskell resources by domain here, and as you can see, there are Haskell bindings to tensorflow and pytorch, along with other libraries that support common data science programming.