Your point is a good one: Exotic, difficult languages attract enthusiasts with deeper skills than a median developer.
Haskell also has a confounding issue: Because of its focus on research and experimentation, standard best practices don't exist like they do in languages optimized for real-world use. Because there are few community guidelines, engineering practices vary widely, and unfortunately at the same time, Haskell's public codebase resources are small.
To see this playing out, check out Haskell discussions about choosing a SQL library, effect system, monad transformer stack, or web framework. The answers are all over the map because that diversity is baked into Haskell's culture and practice.
Was going to ask what is the Numpy equivalent in Haskell. Cause Python has a thousand decent examples of how to do everything you might want to do in Numpy.
Haskell also has a confounding issue: Because of its focus on research and experimentation, standard best practices don't exist like they do in languages optimized for real-world use. Because there are few community guidelines, engineering practices vary widely, and unfortunately at the same time, Haskell's public codebase resources are small.
To see this playing out, check out Haskell discussions about choosing a SQL library, effect system, monad transformer stack, or web framework. The answers are all over the map because that diversity is baked into Haskell's culture and practice.