Yes, it was some luck ... and a lot of hard work. But it wasn't only NumPy that helped. I was around in the 1990s when Python started making waves in the scien

Yes, it was some luck ... and a lot of hard work. But it wasn't only NumPy that helped. I was around in the 1990s when Python started making waves in the scientific computing and research community. Python wasn't installed by default (we mostly used IRIX back then). Python was excellent for "steering" low-level computations, to use the buzzword at the time. That is, if you had a large body of existing C, Fortran, whatever code, you could turn them into Python extensions, let Python glue the components together, and use a Python program/script to organize the high-level data- and control-flow. Swig was a popular tool to help automate that process. Swig 1.0 had support for Tcl, Perl, Guile and Python, back in 1996. I used Swig to interface to an existing cheminformatics toolkit, and published my experience at https://www.drdobbs.com/cpp/making-c-extensions-more-pythonic/184404439 https://www.drdobbs.com/cpp/making-c-extensions-more-pythoni... . Python made it very easy to convert the implicit object model of the underlying C toolkit into an explicit OO model in Python. Python's reference-counted garbage collection was also a good fit to the C library. Together these helped simplify scientific research work quite a bit. And that's without NumPy. Even now I do a lot of non-numerical scientific software development, and rarely have need for NumPy. While I often use Python to steer components written in C or C++.