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I call it a "bizarre blind spot" because it seems like there's a silent consensus to never talk about this basic fact. It's a bit surreal attending SciPy and hearing all of these people talking about scientific computing in Python when almost every single person in the room spends the vast majority of their time and energy writing C code. I disagree that the separation between implementation and user-land that's enfo…
> You can use NumPy until the cows come home and you will be no more qualified to contribute to its internals than you were when you started. Just for whatever it's worth, as an occasional contributor to numpy who is an absolutely terrible C programmer, there's a _lot_ you can contribute with pure python. Yes, the core of the functionality is in C, but most of the user-facing functionality isn't. That having been sai…
I completely agree that Julia and SciPy are complementary rather than competing. I've attended the SciPy conference for several years and it's great – I love the Python and SciPy communities. It's definitely crucial to both be able to easily call existing C and Fortran libraries and write code in the high-level language that's as fast as it would have been in C. You don't want to reimplement things like BLAS, LAPACK and FFTW – but you do want to be able to implement new libraries without coding in Fortran or C, and more importantly, be able to write them in a very generic, reusable fashion.