Limited Missing Value Support | Teradata Python Package - Limited Missing Value Support - Teradata Package for Python

NaN and +/- Inf values can arise in floating point calculations. They are rendered when a DataFrame is evaluated.

>>> df
           value
row_id       
2           -inf   
1            inf
3            NaN
 
>>> df.dtypes
row_id       str
value        float

NaN and +/- Inf values are not supported as missing values. Particularly, there is no support to reference these values in the Analytics Database. Only the NULL value is supported as a missing value, in which case they are usually rendered as None. Floating point columns with NULL values can be rendered as NaN. In this case, NaN is recognized as a missing value.

>>> df[df.value.isna() == True]
 
 
           value
row_id      
3           None   
 
>>> df[df.value.isna() == False]
 
           value
row_id      
2           -inf
1            inf