In the pure Python cases the 8 bytes per attribute are just pointers. The x, y and z are themselves full-blown objects with all the extra memory overhead that comes with it and this is not counted in the article. For example, an int object uses 28 bytes, so three of them already use up more than each of the described container objects.

The Cython and Numpy cases directly store the actual data and this has the larger effect to reduce memory.