[vukonen]

The reason to choose it in the first place is, as I said, ease and convenience of use when used as a glue language. It's easier than in say, C or C++, to call some functions operating on Numpy arrays and make a graph based on that.

I just think saying "the person who created Numpy chose Python" is a bit weird, when all the heavy-lifting code is in C. Following this logic that person also chose all the other languages that have bindings to Numpy. Yes, the creators of Numpy probably had Python in mind when creating the library. But that just means they chose Python as their glue language of choice, not as their platform for implementing algorithms used in ML or scientific computing.

I did say that being easy to learn is a great language feature that made Python succeed in scientific circles. It's just that using Python for science exposes its shortcomings much less often that using it more generally, which is in most cases done by "full-blown" software engineers, who spend more time programming than, for example, creating a scientific model.

[logicprog]

Yeah I mean I agree with you on basically all of this, I just think this proves my point that to whatever extent languages are used, it's more about features of the language, not killer apps.

And yeah, saying the author of Numpy chose Python is a little weird, since it's mostly written in C and Fortran (IIRC) but I'm pretty sure it was made with bindings to Python in mind, as you say, so the point still stands.