Pandas - dropping rows with missing data not working using .isnull(), notnull(), dropna()

Your example DF has NaN and NaT as strings which .dropna, .notnull and co. won't consider falsey, so given your example you can use...

df[~df.isin(['NaN', 'NaT']).any(axis=1)]

Which gives you:

   A  B  C
0  1  1  1
1  2  2  2
4  5  5  5

If you had a DF such as (note of the use of np.nan and np.datetime64('NaT') instead of strings:

df = pd.DataFrame({'A':[1,2,3,4,5],'B':[1,2,np.nan,4,5],'C':[1,2,3,np.datetime64('NaT'),5]})

Then running df.dropna() which give you:

   A    B  C
0  1  1.0  1
1  2  2.0  2
4  5  5.0  5

Note that column B is now a float instead of an integer as that's required to store NaN values.

Try this on orig data:

Test.replace(["NaN", 'NaT'], np.nan, inplace = True)
Test = Test.dropna()
Test

Or Modify data and do this

import pandas as pd
import numpy as np 

Test = pd.DataFrame({'A':[1,2,3,4,5],'B':[1,2,np.nan,4,5],'C':[1,2,3,pd.NaT,5]})
print(Test)
Test = Test.dropna()
print(Test)



   A    B  C
0  1  1.0  1
1  2  2.0  2
4  5  5.0  5