I'll be a bit pedantic. The original essay says "pure Python", which is not a technology but a language. There are several implementations of Python, which I'll argue is the "technology". The PyPy implementation of the pure Python algorithm is 10x faster than the CPython implementation.
According to the original essay, that's close to the CPython/NumPy performance, and faster than the CPython/Pandas version.
It's tempting to omit PyPy because "nobody uses it in data science". That's a self-fulfilling argument.
PyPy is doing amazing work to support both NumPy and Pandas, but it's limited by funding. Why aren't data science people pumping money into PyPy? It would give a huge performance boost even for naive, throw-it-together algorithms.
If you don't know why PyPy can do for you, how do you learn if/when you should use it?