I use numpy & pandas, lists & dicts every day. I read your docs/github page, but can you help me see the value?
However, I do think there are lots of common tasks that need to be done with lists that should be methods rather than fancy footwork =)
For example: https://stackoverflow.com/questions/3462143/get-difference-b...
As you allude to w your zip loop: https://stackoverflow.com/questions/1919044/is-there-a-bette...
Naturally if you're dealing with big arrays/tensors, numpy is the best choice for operating on sequences.
However, ndarrays have downsides for certain use cases - as ndarrays are fixed size, adding elements is very slow, also they don't support functional methods (or rather you have to create a new array every time you apply e.g. a map), and ndarrays of any other type than numbers doesn't really make sense.
Many of the methods are wrappers for built-ins, but I find the syntax of Arrays cleaner than the weirdness of the builtins.
For example, while applying an async "starmap" to an Array is just a method call, with built-in lists you would have go through the whole hassle of importing both ThreadPoolExecutor and starmap, creating an executor, scheduling the function, and finally converting the result back to a list.
That resonates with me now that you explain that I can't do it.
I do like chaining things in pandas like `df.select_types("float").head(100).plot.hist()`