[vukonen]

I don't think anyone has "chosen" to implement Numpy (or any of the hundreds of C and C++ modules) in Python because of its language features. On the contrary, they specifically avoided using pure Python because of its low performance and opted for more performant languages used through FFI.

The reason people have gone through the effort to implement those modules is absolutely because of "captive audience based killer apps" which for Python were ML and scientific computing. And while Python is decent in its role as a glue language, it succeeded mostly by being easy to pick up and very forgiving, which shouldn't be the main factors for choosing a language for anyone who is a software engineer and not a scientist.

[logicprog]

But what made the people who chose it for ML and scientific computing choose it? What made it better for that? There has to be a reason to choose it in the first place, and before Numpy it wasn't awesome at ML and scientific computing that I know of.

Also, being easy to learn is very much a language feature that put it in good standing for non-software-engineers, and I'm not sure why you brought up if it's good for software engineers.

[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.

[rusticpenn]

Actually, Matlab pulled educational licenses from research institutions like Fraunhoffer. This created a need for an alternative to Matlab. This made python popular in ML and scientific communities.