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 Advanced SQL Engine. 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