Not GP, but I’m using pandas daily to build up a BI platform within a financial institution. Compared to Matlab and even Fortran it has some issues IMO: * why

Not GP, but I’m using pandas daily to build up a BI platform within a financial institution. Compared to Matlab and even Fortran it has some issues IMO: * why distinguish between Series and DataFrame? just give me an interface for m x n matrices or even higher dimensions. * pure vs. in-place operations. not such a big fan of having multiple versions of the same function, e.g. a more pythonic df[“my_col”] = series vs. a more functional df.assign({“my_col”: series}) ; I’d rather have everything like the latter to be able to more easily have best practices in place. That brings me to another point: if we keep everything purely functional, then python’s syntax is making things a bit awkward. Where in something like JS you could just put every function call with its dot on a new line without the need to assign, in Python this requires putting line break characters or wrapping it in round brackets. This is one place where a language with explicit assignment terminators (semicolons) are a bit cleaner to work with. All that being said scipy is still a great choice to have both system programming and numerical business logic in one language.