In the short term Numba is much more practical for numerics. In the longer term Pyston looks promising - it's actually similar to Numba in that it also uses LLVM, I imagine there could be synergy between the two...
In the short term Numba is much more practical for numerics. In the longer term Pyston looks promising - it's actually similar to Numba in that it also uses LLVM, I imagine there could be synergy between the two...
Pandas is one, others include scikit-learn, scikit-image, Astropy, Bioinformatics libraries, stats libraries, etc... which all have heavy C/Cython use and depend to varying degrees on the Python C-api. Porting NumPy barely scratches the surface of scientific python.
Going on a tangent, and though I realize it's a lost battle, I wish people would stop saying that NumPy is the base of scientific programming in Python. As Biopython shows, it isn't required for at least some of bioinformatics.
My own research[1] deals with chemical graphs, and NumPy/SciPy/etc. are nearly irrelevant to that research.
[1] For example, given a set of 100 structures, what is the largest substructure (based on the number of bonds) which is in at least 90 of the structures?