[Don't use `is` to check for NaN in Python] Always use `np.isnan(.)` #python

nan_tripup.py

>>> import pandas as pd
>>> import numpy as np
>>>
>>> s = pd.Series([10, 11, 12])
>>>
>>> s[2] = np.nan
>>> s[2] is np.nan
False

# A NaN type can get coerced which is what happened above, and when this happens, a separate instance is created that thus has a different id(.) which maks the `is` check fail
>>> type(np.nan)
float
>>> type(s[2])
float64

# Always use `np.isnan(.)`
>>> np.isnan(s[2])
True