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Comment by hyperbovine | Hacker News Reader

> Does anyone know why it still hasn't managed to get enough traction to become the official path for the language

The people who really care about Python performance (sci. comp & HPC) are the ones driving the (non-)adoption of PyPy. One, the existing solution (CPython + Cython) is pretty mature. Two, they require the holy trinity: Numpy, Scipy and Matplotlib. None of these are supported on PyPy, and only Numpy is even close.

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numpy works, scipy & matplotlib are very close. Stop spreading false information please.

That said, the cpython+cython solution is not going away any time soon.

> numpy works, scipy & matplotlib are very close.

Care to elaborate? I agree that numpypy is pretty much complete, though I have not found it to be faster in my limited testing. I was under the impression that a great deal of work would be required to get the other two running, even using CPyExt (which would not be faster, which was the whole point to begin with). I'd love to be proved wrong though.

Real world use cases are large and messy. Now that the cython/pandas/numpy stack works out of the box on PyPy, we can begin to see how often the mix of python code (jittable, fast) and c code (fast) needs to cross the c-to-python barrier (slow). There may be some cases, like NLP where speeding up parsing and io can make a difference

numpy, pandas (mostly) and (scipy) mostly work through cpyext. We're considering how to make them fast now. It would never be faster on large_array + large_array, but it should be faster (by a lot) with element access etc., bit like numba

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