Missing values in data can be handled by using the isna() or notna() methods.
Currently, the only NA value supported is None. 'isnull' and 'notnull' are aliases of 'isna' and 'notna' respectively. Other possible NA values, +Inf, -Inf, and NaN (typically seen in floating point calculations) are not supported. See Limited Missing Value Support.
Example: Handle missing values
>>> df = DataFrame('sales')
>>> df
Feb Jan Mar Apr datetime accounts Alpha Co 210.0 200 215 250 04/01/2017 Blue Inc 90.0 50 95 101 04/01/2017 Yellow Inc 90.0 None None None 04/01/2017 Jones LLC 200.0 150 140 180 04/01/2017 Red Inc 200.0 150 140 None 04/01/2017 Orange Inc 210.0 None None 250 04/01/2017
>>> df.assign(drop_columns = True, January = df.Jan, NullJanuary = df.Jan.isna())
January NullJanuary 0 200 0 1 50 0 2 None 1 3 150 0 4 150 0 5 None 1