> In my experience I've found real-world applications, by definition are never as pure and perfect as we like them to be. Users do stupid things, or vendor APIs don't quite fit what we want.

This is actually one of haskell's greatest strengths IMO -- it enforces the kind of discipline that sidesteps this problem completely. The kind of functions you write with haskell don't allow bullshit -- non-nullable types are the biggest example of this I can think of, there's also the pervasive use of `Maybe Value` and `Either SomeError Value` types.

In other places it's usually referred to as Domain Driven Design or the onion architecture -- but best practices for software development usually dictate that you get rid of bullshit as early as possible on the borders of your application. Put simply, don't let invalid/incorrect input make it into your system.

> And personally it's why I think the Haskellers I know often get stuck in the mud. Seems the language, or just the Haskellers I know, are better suited for a academic applications.

Totally true, we do get stuck in the mud, but it's such nice mud -- the things you worry about are just different (and I think this is what you're seeing). Roughly zero haskellers are worrying about NPEs -- there's no urge to get something to "just work" because "better" is right there, and haskell encourages you to strive for it.

That said, it's absolutely the case that a ton of energy in haskell land is spent on academic pursuits, but that's actually good imo, languages that don't do this get stale.

> I'd love to be proven wrong as I'm into type safety and FP, in general.

Well I don't know that this is something someone else can prove to you, outside of people just writing more middling/regular software in haskell and getting it out there, which is a bit of a community thing. I do my best to write practical haskell software and write about it, but a bunch of my projects aren't open source (just yet -- I'm heavily considering making one of them open source right now). Rust is also proving to be a huge (welcome) distraction because it gives much of the haskell creature comforts with within-C++/C range efficiency.

Maybe take haskell for a spin? The learning curve is high but it will bend your mind in a good way. Or if you're of the web persuasion, try Elm/Purescript, they're thoroughly practical.

Limited nominal typing, limited support for record-like functionality (ability to handle datastructures generically-but-safely), no HKT (so difficult to handle secondary concerns in the type system - e.g. writing a function whose type enforces that it's called within a database transaction, but you can still compose it with other such functions and run them all together in a single transaction). I wanted to like TypeScript, I really did, but after a month or so I was fed up enough to actually put nonzero effort into building with Scala.js (which turned out to be really easy) and within an hour I had the safety properties I was used to and was more productive as a result. (Scala isn't Haskell but the advantages are similar).

Exactly, which is why Haskellers should listen to and respond to feedback rather than asserting it is the feedback-giver that is wrong.

> For me a "good tool" is robust, reliable, and easy to fix. For most of the data scientists I know, a "good tool" is one where "a single easy to remember command magically does the thing I want in one go".

I don't agree with this statement and I think demonstrates some of why Haskellers are kind of frustrating. Most data scientist (and most people in general) want to focus on solving the problem they're tasked with, not elegantly and efficiently positioning themselves to be able to do so. Jupyter and Numpy/Pandas are great because they allow the user to focus exclusively on solving their problem, not on language or framework-level concerns. This is not "a magic command that does the thing I want in one go." It is the separating the needs of the hammer maker from the needs of the carpenter.