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floating point - Why is NaN not equal to NaN? - Stack Overflow 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
How do you check that a number is NaN in JavaScript? 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:
What is the difference between NaN and None? - Stack Overflow NaN can be used as a numerical value on mathematical operations, while None cannot (or at least shouldn't) NaN is a numeric value, as defined in IEEE 754 floating-point standard 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
python - How to check if particular value (in cell) is NaN in pandas . . . >>> df iloc[1,0] nan So, why is the second option not working? Is it possible to check for NaN values using iloc? Editor's note: 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
javascript - Is NaN equal to NaN? - Stack Overflow 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
python - What is inf and nan? - Stack Overflow 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
Linear regression of arrays containing NAN values slope, intercept, r_value, p_value, std_err = stats linregress(varx, vary) results in NaNs for every output variable What is the most convenient way to take only valid values from both arrays as input to the linear regression? I heard about masking arrays, but am not sure how it works exactly