PyPy2 can now import and run many C-extension packages, among the most notable are Numpy, Cython, and Pandas. Performance may be slower than CPython, especially for frequently-called short C functions. Please let us know if your use case is slow, we have ideas how to make things faster but need real-world examples (not micro-benchmarks) of problematic code.
The problem besides the jit not being able to inline external functions is that the CPython structures, and especially the refcounting used in CPython is problematic [1]
[0]: Actually specifically numpy is being rewritten in PyPy as a RPython Mixed Module: http://doc.pypy.org/en/latest/extending.html#rpython-mixed-m... [1]: http://doc.pypy.org/en/latest/discussion/rawrefcount.html
I've tried optimizing sympy and it's hard - there is a chance that it uses cython (which will slow things down on pypy) but also the gains were small (up to 2x) and only noticable if you run it for a while (at least few seconds).
SymPy is a bit of an example that while python as a language is not necesarilly slow, since you can make a plan for most parts, it breeds culture of unnecessary meta-programming, tons of copies and dict lookups that makes it REALLY HARD to optimize. ORMs go in the same category