mtl-style-example VS post-rfc

Re: effects libraries, it's probably worth starting with e.g. https://www.fpcomplete.com/blog/2017/06/readert-design-pattern/ https://www.parsonsmatt.org/2018/03/22/three_layer_haskell_cake.html https://github.com/lexi-lambda/mtl-style-example if you're just getting familiar with the ecosystem. I'll add, anything written by the the people I linked to in this comment is probably worth reading as well.

Personally, it felt to me like, once I really understood monad transformers (https://hackage.haskell.org/package/transformers) and mtl (https://hackage.haskell.org/package/mtl), the reasons they both exist and how they are distinct, how you use them generally and what common patterns are around structuring your app (see e.g. https://github.com/lexi-lambda/mtl-style-example and the 3-layer-cake link someone else provided here for two), a lot of stuff in the ecosystem suddenly became comprehensible and useful to me, in a practical, "real world" way. In fact I'd go so far as to assert that getting comfortable with mtl in particular is the biggest single step you can take to being able to build arbitrarily useful real-world apps that are no longer toys.

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.