I know much less about R, but the whole Python/NumPy thing works butThere will sure be some algorithms where numba/cython would do better (especially if they cannot be easily vectorized), but that's not the point. The thing about numpy is that it provides a unified accepted interface (plus a reasonable set of reasonably fast tools and algorithms) for arrays and buffers for a multitude of scientific libraries (scipy, pytables, h5py, pandas, scikit-*, just to name a few), which then makes it much easier to use them together and write your own ones.only because it is faster and easier than Python alone. NumPyperformance is actually quite poor. I am finding I can write Python + Numba code that hugely outperforms that same algorithm using NumPy.
On Saturday, 27 December 2014 at 10:54:01 UTC, Russel Winder via
Digitalmars-d-learn wrote:
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