Otherwise by identity nan/nan should equal 1, along with all the other consequences like (nan/nan)==1, (nan*1)==nan, etc. Sure, but i did ask how to check that a number is nan, as opposed to any value. Float('nan') represents nan (not a number) But how do i check for it? 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.
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 a sudden a nan appears
>>> 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 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. 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. I wonder what is the rationale for reserving so many useful values, while
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