As an engineer, I can quickly knock together behavioural models of electronic circuits, complete units, and control systems in Python, then annoyingly in a few recent cases, have to re-write in C for speed.
I've tried PyPy, the just-in-time compiler for Python, and that is impressively, hugely fast in comparison, but it's no good making these models if I can't display the results in a useful way, and at the moment PyPy just doesn't have the huge range of useful time-saving libraries that CPython has. It's still quicker to do a re-write in the more cumbersome C than try to work with PyPy because C, like CPython, also has many useful libraries. A few years back, I recall people saying that PyPy was going to be the future of Python, but it seems to me that CPython still has the lion's share of the momentum, is developing faster and has ever more libraries, while PyPy is struggling to get enough workers to even get Numpy completed. Maybe there's not enough people like me that have really felt the need for the speed. Or maybe it's simply the accident of the historical development path that's set-in-stone an interpreter rather than a JIT. Anybody got a useful perspective on this? -- https://mail.python.org/mailman/listinfo/python-list
