Except that you do. Python is a form of OOP/Iterative programming, involves memory allocation, pointers, references, and the like. Pandas requires the skilled application of a general purpose language coupled with a domain-specific vocabulary for your problem space.
I maintain that APL isn't fundamentally more difficult. If there is something that you have as an example that you think is just too "hard" to do in APL that is very "easy" in Python and Pandas, please do let me know via email (arcfide@sacrideo.us) as I would love to make sure APL has a good answer to such things (I want to make APL accessible).
Since you are talking about statistics and data analysis type things, you may find TamStat of interest:
Additionally, don't forget that Dyalog APL also has a full Chart/Graphics system (two, actually), and a large suite of idiomatic expressions for computing many classic analysis problems. There's a number of other integrations that people seem to be unaware of often enough.
I very seriously would like to receive from you one example of something you have done that you find too difficult in APL, because if it really is, then we can do better and I'd like to make sure that we do.
Just seeing this. I'm not the original poster that you replied to, but their point is somewhat valid as is yours.
Python certainly requires one to learn iterative programming which is definitely not a walk in the park. I learned it over a decade ago, but vividly recall how non-intuitive it was at the time (global versus local scope had me scratching my head once upon a time). Then there is lists, tuples, dictionaries, pass by reference, iterators, file I/O...just soo much to learn.
One interesting thing is that I used OO methods in Python for ages before understanding how to write and use classes. To me, a = list.sort() was just a weird way to read a = sort(list). You can almost write Python entirely in an imperative manner and for short scripts I almost always do.
Learning all the OO aspects of Java/C# and all the fundamentals of FP (monads, currying... etc) is much harder for me than APL which is basically just learn what these symbols do and the very straightforward way to combine them and you're done. The only harder part of APL is somehow learning to replace imperative programming with array operations. Sure it is easy to do some things like sum up an array (+/ array), but that is also easy in Python (list.sum()). The idioms as APL people call them are not at all obvious to me, nor is the path of enlightenment outside of stop talking and go write some APL haha.
I maintain that APL isn't fundamentally more difficult. If there is something that you have as an example that you think is just too "hard" to do in APL that is very "easy" in Python and Pandas, please do let me know via email (arcfide@sacrideo.us) as I would love to make sure APL has a good answer to such things (I want to make APL accessible).
Since you are talking about statistics and data analysis type things, you may find TamStat of interest:
http://www.tamstat.com/
Additionally, don't forget that Dyalog APL also has a full Chart/Graphics system (two, actually), and a large suite of idiomatic expressions for computing many classic analysis problems. There's a number of other integrations that people seem to be unaware of often enough.
I very seriously would like to receive from you one example of something you have done that you find too difficult in APL, because if it really is, then we can do better and I'd like to make sure that we do.