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Sure, but i did ask how to check that a number is nan, as opposed to any value. As for nan in [nan] being true, that's because identity is tested before equality for containment in lists. 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

Otherwise by identity nan/nan should equal 1, along with all the other consequences like (nan/nan)==1, (nan*1)==nan, etc. Nan not being equal to nan is part of the definition of nan, so that part's easy Float('nan') represents nan (not a number)

But how do i check for it?

In the land of real numbers, there are some operations that usually can be performed, but sometimes don't have a defined result For example, let's look at logarithms. Javascript automatic type conversion convert nan into number, so checking if a number is not a number will always b false And nan !== nan will be true.

False however if i check that value i get >>> df.iloc[1,0] nan so, why is the second option not working Is it possible to check for nan values using iloc 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.

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. Sometimes the computations of the loss in the loss layers causes nan s to appear Looking at the runtime log you probably won't notice anything unusual

Loss is decreasing gradually, and all of. In numpy there are nan, nan and nan What's the sense of having all three, do they differ or any of these can be used interchangeably?

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