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Comment by ngoldbaum | Hacker News Reader

It makes sense to use pypy if you're writing pure python code. The second you need a C extension, you're pretty much out of luck. This kills a lot of the appeal for people in the scientific/analytics side of things, who make heavy use of legacy C and Fortran routines.

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> The second you need a C extension, you're pretty much out of luck.

In theory, shouldn't CFFI be the foundation of the solution to that problem?

yes

In practice it works pretty well. I am nearing completion of a rewrite of X's XCB-based python bindings in cffi, and it has worked out quite nicely.

You can use these C and Fortran routines on PyPy, just not with the CPython C extension API.

There's a lot of established code built around the C API. It's not like it can just be rewritten using CFFI over a weekend.

Well, I'm not talking about every C extension in the world, I'm talking about thin bindings around C routines which is what numpy has for fft for example, someone wrote a basic equivalent for numpypy in a few hours with no prior numpypy/cffi knowledge.

Unfortunately, NumPy uses deep knowledge of the CPython API in quite a few places, which is one of the reasons implementing NumPyPy has been so challenging.

Another approach should be used for those, but to be clear I wasn't talking about the entire numpy library, I'm talking about things like numpy.fft

No, you are wrong. It supports both ctypes and cffi, both of which should be the goto for calling native code. Use PyObject has been the stupid choice for over 4 years.

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