| - map/reduce/filter/for-comps in the standard library. Go doesn't support this style of programming, and because of the lack of generics, you can't write generic data structures with these types of methods either. It's all loops and mutation in Go - first class functions. Go does have these - concise lambda syntax, that makes them nice/easy to use. Go has first class functions, but a very verbose/awkward lambda syntax - can easily create your own generic data structures with functional interfaces (can't do this in Go b/c no generics) - Python is pretty strongly typed, and if you meant statically typed, there's now optional static type checking in Python, similar to TypeScript (not as robust/well implemented though) - Python has decent immutability support. For example, dataclasses (https://docs.python.org/3/library/dataclasses.html) with frozen=True are a lot like immutable classes in more purely functional languages (i.e. case classes in Scala). Tuples and named tuples. There are libs out there for frozen (a.k.a. immutable) dicts, lists, etc. - Python is about to get pattern matching in 3.10 - functools (https://docs.python.org/3/library/functools.html) - etc. You can absolutely use Python in a very mutable-OO style, but it also has pretty good functional programming support. If you look at most Python data science code, it's written pretty functionally. I'd say most important for data science applications is the ability to create generic data structures with functional interfaces - you can't do this in Go, makes it really awkward to write a lot of the foundational vector, data frame, etc. libraries, that basically all higher level data science libs depend on. |