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Nan is designed to propagate through all calculations, infecting them like a virus, so if somewhere in your deep, complex calculations you hit upon a nan, you don't bubble out a seemingly sensible answer This question previously used pd.np instead of np and.ix in addition to.iloc, but since these no longer exist, they have been edited out to keep it short and clear. Otherwise by identity nan/nan should equal 1, along with all the other consequences like (nan/nan)==1, (nan*1)==nan, etc.

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Float('nan') represents nan (not a number) Is it possible to check for nan values using iloc But how do i check for it?

182 i just came across this technique in the book effective javascript that is pretty simple

Since nan is the only javascript value that is treated as unequal to itself, you can always test if a value is nan by checking it for equality to itself: 37 it's a special case, nan is the only thing in javascript not equal to itself Although the other answers about strings vs the nan object are right too. Nan can be used as a numerical value on mathematical operations, while none cannot (or at least shouldn't)

None is an internal python type (nonetype) and would be more like inexistent or empty than numerically invalid in this context The main symptom of that is that, if you perform, say, an average or a sum on an. Nan stands for not a number, and this is not equal to 0 Although positive and negative infinity can be said to be symmetric about 0, the same can be said for any value n, meaning that the result of adding the two yields nan

This idea is discussed in this math.se question.

Nan not being equal to nan is part of the definition of nan, so that part's easy As for nan in [nan] being true, that's because identity is tested before equality for containment in lists. I would like to know why some languages like r has both na and nan What are the differences or are they equally the same

Is it really needed to have na? I have a program that tries to shrink a double down to a desired number The output i get is nan What does nan mean in java?

>>> df.iloc[1,0] nan so, why is the second option not working