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 the Teradata Package for Python Limitations and Considerations section for more information.
Example
>>> df = DataFrame('iris') >>> df SepalLength SepalWidth PetalLength PetalWidth Name 0 5.800 2.700 5.100 1.90 Iris-virginica 1 6.500 3.000 5.800 2.20 Iris-virginica 2 1.012 1.202 3.232 4.23 None 3 5.400 3.700 1.500 0.20 Iris-setosa 4 6.700 2.500 5.800 1.80 Iris-virginica 5 7.200 3.600 6.100 2.50 Iris-virginica
>>> df.assign(drop_columns = True, Name = df.Name, NullName = df.Name.isna()) Name NullName 0 None 1 1 Iris-virginica 0 2 Iris-virginica 0 3 Iris-virginica 0 4 Iris-virginica 0 5 Iris-virginica 0