My code:

xdf = pd.DataFrame(data={'A':[-10,np.nan,-2.2],'B':[np.nan,2,1.5],'C':[3,1,-0.3]},index=['2023-05-13 08:40:00','2023-05-13 08:41:00','2023-05-13 08:42:00'])
xdf = 
                          A      B      C
2023-05-13 08:40:00     -10.0   NaN     3.0
2023-05-13 08:41:00     NaN     2.0     1.0
2023-05-13 08:42:00     -2.2    1.5     -0.3

Consider only values below 4.0 and above -4.0 in each row of the dataframe

print(xdf[((xdf<4.0).all(axis=1))&((xdf>-4.0).all(axis=1))])

Present output:

                          A      B      C
2023-05-13 08:42:00     -2.2    1.5     -0.3

Expected output: My above code drops a row if there is a NaN in one column, despite other columns satisfying the filter condition. So, I want to omit NaN columns and consider non-NaN columns in <> operation.

                          A      B      C
2023-05-13 08:41:00     NaN     2.0     1.0
2023-05-13 08:42:00     -2.2    1.5     -0.3