I used to be a hardcore functional programming weenie, but over time I realized that to do high-performance, systems programming in an FP language means writing a bunch of non-idiomatic code, to the point that it's worth considering C (or C++ for STL only, but not that OOP stuff) instead unless you have a good reason (which you might) for a nonstandard language.
The problem isn't Python itself. Python has come a long way from where it started. The problem is people using Python for modules where they actually end up needing, say, manual memory management or heterogeneous high performance (e.g. Monte Carlo algorithms).
No, I think it is fair to call out mediocrity, even when it tries to pull the "disclaim exactly the set of specific applications it gets called out on" trick.
Sure, pandas often beats raw python by a bit, but come on, there's so much mediocrity between the two that I doubt they even had to cheat to find a situation the other way around.
Don't use a hammer as a screwdriver.
I'm not even implying they shouldn't have used pandas for this, I'm suggesting they probably wrote the wrong pandas code for this.
Pandas is typically 3 times faster than raw Python, not 10 times slower.