That makes sense, and still to me suggests the OP is inaccurate talking about where numpy's performance advantage came from: > NumPy. NumPy takes a similar app

That makes sense, and still to me suggests the OP is inaccurate talking about where numpy's performance advantage came from: > NumPy. NumPy takes a similar approach: it cannot itself do anything to speed up Python’s fundamental interpreted execution speed3, but it can move the intensive numerical operations that it implements into operations on arrays, rather than operations on individual objects, whether numbers or not. The move to operations on arrays would do nothing if the logic wasn't also happening in the C-written backend -- in fact numpy _did_ do something about Python's fundamental interpreted execution speed, it moved the logic to the C backend. You point out that _also_ needed the move to array-based things to take full advantage of it, to minimize trips across the python/C boundary if I understand right. But the gain is not simply from changing from operations on individual objects to operations on arrays. This is not really a demonstration of the array "style" having amazing performance advantages (5000%!) in general, generalizable to any platform as the OP does. It is instead in the numpy case specific to the enviroment of Python with C bindings, and wanting to minimize trips across the python/C boundary.