I wrote scientific python for several years at a university research project, coming from a statistics background. I wrote a forecasting tool and related plotti

I wrote scientific python for several years at a university research project, coming from a statistics background. I wrote a forecasting tool and related plotting, simulation, ML, evaluation etc tools. The reasons for python’s success are obviously the ecosystem. Numpy is the foundation. On top we have sklearn, statsmodels, pandas, matplotlib. Before our project most work in the department was done in Stata, a proprietary language/tool that works well for some classical regression and stats work but falls apart as soon as things get complicated. Moving to python allowed us, a group of social scientists, to work on some really hard problems. Now we have boosted tree models and other tools that just can’t be used in the old tools like Stata. Python and R run the show in social science.