1) when you're using native libraries via python that were written by better c/c++ programmers than you are and you're spending most of your time within them
2) when you're using native libraries in python that are better implementations than the c/c++ libraries you're comparing against
3) when you don't know the libraries you're using in c/c++ (what they're talking about here)
...otherwise, if you're just using doing basic control flow optimizing compiler c/c++ will almost always be faster. (unless you're using numba or pypy or something).
point stands about the constants though. yes, asymptotic analysis will tell you about how an algorithm's performance scales, but if you're just looking for raw performance, especially if you have "little data", you really have to measure as implementation details of the software and hardware start to have a greater effect. (often times the growth of execution time isn't even smooth with the growth of n for small n)