Scientific Python benefits more from (properly) vectorized numpy code, Cython, Numba and Numexpr than what it gains from PyPy. From my experience, one of the b

Scientific Python benefits more from (properly) vectorized numpy code, Cython, Numba and Numexpr than what it gains from PyPy. From my experience, one of the biggest issues with PyPy is that it's not a magic pill you take and suddenly all your Python code runs faster -- that's how they "sell" it. But in many cases, the idiomatic code for CPython results in slow PyPy execution, and idiomatic PyPy code results in slow(er) CPython execution. That means that you have to refactor your code, and whereas you won't be breaking compatibility with CPython, your CPython performance will (most often) suffer. So, unless you're the sole user of your code, or you have control or decision power over most of your users, (or you just don't care to keep good CPython performance) your project might not benefit: "yeah, in PyPy it runs 2x faster, but 80% of our clients use CPython and are complaining over a 5x slow down -- ditch that code and get back to the previous version" may end up being the feedback from someone above you in hierarchy.