Nan S Mashed Potato Salad Noshing With The Nolands 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. @gothdo: sure, but i did ask how to check that a number is nan, as opposed to any value.
Nan S Mashed Potato Salad Noshing With The Nolands 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. The string "nan" is a possible value, as is an empty string. i managed to get pandas to read "nan" as a string, but i can't figure out how to get it not to read an empty value as nan. 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? 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.
Noshing With The Nolands The string "nan" is a possible value, as is an empty string. i managed to get pandas to read "nan" as a string, but i can't figure out how to get it not to read an empty value as nan. 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? 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. However, my blank records are always written as 'nan' to the output file. (without the quotes) i read the excel file via method read excel (xlsx, sheetname='sheet1', dtype = str) i am specifying dtype because i have some columns that are numbers but should be treated as string. (otherwise they might lose leading 0s etc) i.e. To remove nan values from a numpy array x: x = x[~numpy.isnan(x)] explanation the inner function numpy.isnan returns a boolean logical array which has the value true everywhere that x is not a number. since we want the opposite, we use the logical not operator ~ to get an array with true s everywhere that x is a valid number. lastly, we use this logical array to index into the original array x. 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. When calling double.isnan() with double.positiveinfinity as argument, the result is false. this is against my intuition since infinity is not a number. apparently "nan" only exists in terms of a co.
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