People don't realize what a revelation Python 1.x was back in the day. Around 97 or so I was tasked with porting a giant mathematica program for calculation dif

People don't realize what a revelation Python 1.x was back in the day. Around 97 or so I was tasked with porting a giant mathematica program for calculation diffraction grating efficiencies into something which ran open source (there was no free mathematica engine for running scripts pack then). Back then, that meant either C or Fortran. Sure, stuff like Perl existed; nobody thought of it as a real interpreter that could be used to construct complicated things any more than Awk was. When I realized Hugunin over at Livermore had done lapack extensions for Python (whatever numpy was called back then) ... well this massive job was done in a week and worked the first time. The winning thing python had that nothing else had at the time was it was social, it was readable, and there was generally only one way to do things: the right way. It no longer has the latter quality, and the preferred coding style in it seems to be java-ish OO-spaghetti, but it's still pretty good. That said, these days, I resent every damn time I have to use it. It's eating data science, more or less because pandas and scikit is ... mostly good enough, and because unlike R it's .... mostly good enough to deploy in an enterprise application. But if you're working on the exploratory side of data science, Python is shit compared to R. Doesn't have the tooling, doesn't have native facilities, and is vastly more long winded. All the attempts to make Python more ... X ... are probably a mistake also. You're taking a beautifully simple tool and making it more exotic and complex. It's like trying to use Matlab to build webservers.