Nobody would really use a compiled language for this, the compile-run-edit-cycle just takes too long. Prior to Python people really just used MATLAB and Mathematica for that sort of work in the physics/engineering side, and R and Stata/SPSS on the bio + maths side. MATLAB, Mathematica, Stata and SPSS are all commercial and R has exactly the same problems to Python in environment management and compiled binaries, if you use it today you end up doing a lot of manual compilation of dependencies and putting them in the PATH on Linux at least.
Python became popular because the key scientific ecosystem libraries copied the libraries from MATLAB closely which made it easy to pick up, and because it was free. Anaconda made a distribution that was easy to install with all the dependencies compiled for you, which worked on Linux/Mac/Windows which made it much easier to use than R. The other interactive languages around at the time were Ruby which was heavily web dev focused, and Perl. Node didn’t yet exist.
Once you have an ecosystem in a language it’s very hard to supplant. You need big resources to go against the grain. That no big company has decided to pour lots of money into alternatives even despite the problems probably tells us that it’s not viewed as being worth it.
Python became popular because the key scientific ecosystem libraries copied the libraries from MATLAB closely which made it easy to pick up, and because it was free. Anaconda made a distribution that was easy to install with all the dependencies compiled for you, which worked on Linux/Mac/Windows which made it much easier to use than R. The other interactive languages around at the time were Ruby which was heavily web dev focused, and Perl. Node didn’t yet exist.
Once you have an ecosystem in a language it’s very hard to supplant. You need big resources to go against the grain. That no big company has decided to pour lots of money into alternatives even despite the problems probably tells us that it’s not viewed as being worth it.