| The use of python in most ML/AI research is not even a "glue" language. It is used as a shell. It's merely an interface to some gigantic, highly optimized libraries (numpy, scipy, and later, Tensorflow, Pytorch, etc.), and it does a very decent job at being an interface. - The language is easy to grasp, at least the part that is used in data science and ML; - The syntax is "familiar", as compared with R; - There are many more general purpose libraries in Python than in R; - There's no memory management problems; - The standard library is packed with batteries; - No compiling, which is important for being a shell; - It's better than bash etc. at dealing with non-text data, especially numerical values; - The community was already writing extensions in C; Some other language could work well, too, had someone written a numpy for it at the time. But there really aren't that many people who are capable, interested, and invested enough to write such a marvelous library. |