Earlier quoted context omitted.
Neither is obvious in either community... And this isn't the reason Python succeeded. Nor is this the reason for the particular change. The reason Python succeeded with data-science is NumPy and related group of libraries. They happened to be the first to offer easy access to R-like features of other statistically-flavored languages in an all-purpose language. I.e. it makes it easy to combine general-purpose code wit…
Surely 1/2 = 0.5 is what a statistician would expect? Whereas 1/2 = 0 is what happens in C, C++, Ruby, Java, C#, F#, Rust... Essentially most of the popular programming languages with the exception of Python and JS/Typescript. Anyway. Maybe my original comment was poorly phrased, but I was not implying that Python succeeded because of this form of catering. Rather, the designers took note of Python becoming popular i…
But, none of that is really relevant. Both operations are useful and common in statistics. Which one is more common will depend on your domain.
> the designers took note of Python [...] made changes
That's putting too much faith in designers of Python. Even calling these people "designers" is giving them too much credit. By their own admission they don't have any sort of vision or strategy for how to deal with the language, they just add random stuff and see if a lot of people complain or thank them.
In other words, matrix multiplication operator is there not because there was some kind of intention or design on the part of the small group of people who are responsible for releasing the language, it was more of a "genetic algorithm" kind of thing: change - iterate - see if change optimizes some metric - repeat.