Exactly my thoughts, if the code is slow after algorithmic optimizations I would go directly for pypy, Cython, Numba, Pythran, etc. In my opinion it makes little sense to optimize pure Python.

Errr, did the article change since you read it?

"At Magnetic, we’ve switched from running all of our performance-sensitive Python code from CPython to PyPy, so let’s consider whether these code optimizations are still appropriate... PyPy is incredibly fast, around an order of magnitude faster than CPython in this benchmark. Because PyPy performs more advanced optimizations than CPython, including many optimizations for classes and methods, the timings for the class vs. closure implementations are a statistical tie."

Why I said is that I would go directly to pypy and related, without first trying to optimize like they did in Python world. They guys at Magnetic came to same conclusion in the end: "Curiously, the function implementation is actually slower than the class approach in PyPy." I don't find this surprising anymore because it is what almost always happens in my code.

I think the techniques discussed here have value, pedagogical and otherwise. Perhaps one is working on a codebase where we don't have the luxury to move to pypy or similar (e.g., my current one which uses some incompatible libraries, so transition would be nontrivial).

The fact that that method accessors are much slower than properties, or that closures can speed things up, is something that we might not always be mindful of, and that new python programmers might not think through.

Ah! That makes sense. Thank you.