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I have tried PyPy once and it gives huge speed up. I am not sure how it works with Pandas/Numpy. I have also tried Numba JIT. Eeasy to use, but in my experince it saves just up to 10% of time. There is also Cython (https://pandas.pydata.org/pandas-docs/stable/enhancingperf.h...) and it definitely works with Pandas. Have not tried yet.

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If you have a pure Python implementation, then why does it matter how well PyPy works with Pandas and NumPy?

My point is a minor one. "Python" isn't a technology. "CPython" and "PyPy" are technologies. Or you can try MicroPython, Iron Python, Jython, and others.

In any case, you'll see the best idiomatic Pandas code is reported to be 500x faster than the original, while the best idiomatic Python is reported to be 950x faster. The proportions are not the same, so it's not like "dividing by the same denominator".

The rank order is unchanged, though that's a weaker observation than what you wrote.

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