both floating as well as in non-floating point arrays it is just used as sentinel that can be easily detectedin [ ]string_data series(['aardvark''artichoke'np nan'avocado']in [ ]string_data out[ ] aardvark artichoke nan avocado in [ ]string_data isnull(out[ ] false false true false the built-in python none value is also treated as na in object arraysin [ ]string_data[ none in [ ]string_data isnull(out[ ] true false true false do not claim that pandas' na representation is optimalbut it is simple and reasonably consistent it' the best solutionwith good all-around performance characteristics and simple apithat could concoct in the absence of true na data type or bit pattern in numpy' data types ongoing development work in numpy may change this in the future table - na handling methods argument description dropna filter axis labels based on whether values for each label have missing datawith varying thresholds for how much missing data to tolerate fillna fill in missing data with some value or using an interpolation method such as 'ffillor 'bfillisnull return like-type object containing boolean values indicating which values are missing na notnull negation of isnull filtering out missing data you have number of options for filtering out missing data while doing it by hand is always an optiondropna can be very helpful on seriesit returns the series with only the non-null data and index valuesin [ ]from numpy import nan as na in [ ]data series([ na na ]in [ ]data dropna(out[ ]handling missing data www it-ebooks info