text VS post-rfc

String is a linked list of UTF-32 codepoints, which is just as bad as it sounds. Haskell programmers usually use Text from the text package instead.

This is the first release that was executed via the new GitHub CI. Everything went smoothly, except for an unexpected packaging bug on windows due to text-2.0 linking against libstdc++ by default.

In addition to what's mentioned, the "default" libraries people use are often not the best-performing ones. E.g. Data.Vector.Hashtables is often much faster than Data.HashMap.Strict (which again is typically faster than Data.Map). And we find performance papercuts in common libraries that may simply be due to not enough people optimising for speed.

Used by nearly every Haskell project: https://hackage.haskell.org/package/text 60% of the modules are ".Internal".

text

Here is a representative patch for text: https://github.com/haskell/text/pull/365/commits/37a2157245457a287d638bbceb472fe93b71f224 Hopefully it clarifies why achieving C performance is problematic even if you have enough primitive operations.

I want to run an app in a Docker container, but detached. This causes getLine to throw an EOF exception, since the terminal detaches. That is, if I understand this thread correctly https://github.com/haskell/text/issues/258.

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.