> If one wants to have all NaNs in one equivalency class
> (e.g. if used as a key-value for example in pandas) it
> is almost impossible to do so in a consistent way 
> without taking a performance hit.

ISTM the performance of the equivalent class case is far less important than the one we were trying to solve.  Given a choice we should prefer helping normal unadorned instances rather than giving preference to a subclass that redefines the usual behaviors.  

In CPython, it is a fact of life that overriding builtin behaviors with pure python code always incurs a performance hit.  Also, in your example, the subclass isn't technically correct because it relies on a non-guaranteed implementation details.  It likely isn't even the fastest approach.

The only guaranteed behaviors are that math.isnan(x) reliably detects a NaN and that x!=x when x is a NaN.  Those are the only assured tools in the uphill battle to fight the weird intrinsic nature of NaNs.

So one possible solution is to replace all the NaNs with a canonical placeholder value that doesn't have undesired properties:

    {None if isnan(x) else x for x in arr}

That relies on guaranteed behaviors and is reasonably fast.  IMO that beats trying to reprogram float('NaN') to behave the opposite of how it was designed.