Python was already big for scientists back in 2010. I remember working on Python tooling at the time, and DS/ML was one of our single largest user groups. It was already popular enough to have an IDE specifically for scientific Python use: https://en.wikipedia.org/wiki/Spyder_(software).
As far as I could tell, it had to do with two things. First, Python is notoriously dynamic and extensible, making it possible to implement "sloppy" syntax like advanced slicing or dataframes. But also, those guys had lots of pre-existing C and Fortran code, and Python had one of the easiest extensibility APIs to wrap it as high-level packages. And with IPython, you had a nice REPL with graphing to use all that from, and then of course notebooks happened.
As far as I could tell, it had to do with two things. First, Python is notoriously dynamic and extensible, making it possible to implement "sloppy" syntax like advanced slicing or dataframes. But also, those guys had lots of pre-existing C and Fortran code, and Python had one of the easiest extensibility APIs to wrap it as high-level packages. And with IPython, you had a nice REPL with graphing to use all that from, and then of course notebooks happened.