Comment by nis251413

I mean, the article and discussion are about numpy's syntax around vectorised code and problems people have with that. Many comments make comparisons with matlab, and I am pointing that a language allowing you do use arrays and write vectorised code, and a language being an array language are not the same thing. Writing vectorised code in a language were everything is based on arrays is in general more natural. The variable definitions are simpler (you do not specify what is array and what is not because everything is an array), and operations tend to work more consistently. Eg in matlab operations are by default done column-wise, because that's how the language is designed and works internally. So functions acting on 2+D arrays act by default column-wise, it does not depend on other context, and they are designed so in order to be faster, not merely consistent to the user. Consistency comes from how arrays are internally represented in memory and the need to have fast code, not just as an arbitrary design choice at the highest level.

Most developers do not touch array languages but I guess most developpers don't in general (need to) vectorise code this way and avoid loops (because they work in other problem domains, use lower level languages etc). If anything, not all problems can be vectorised anyway (or at least elegantly). But if one writes vectorised code, doing that in an array language makes more sense.