Drop NaN containing rows in pandas DataFrame with column condition

Use boolean indexing with inverted mask by ~:

np.random.seed(2021) df = pd.DataFrame(np.random.choice([1,0,np.nan], size=(10, 4)), columns=list('ABCD')) print (df) A B C D 0 1.0 0.0 0.0 1.0 1 0.0 NaN NaN 1.0 2 NaN 0.0 0.0 0.0 3 1.0 1.0 NaN NaN 4 NaN NaN 0.0 0.0 5 0.0 NaN 0.0 1.0 6 0.0 NaN NaN 1.0 7 0.0 1.0 NaN NaN 8 1.0 0.0 1.0 0.0 9 0.0 NaN NaN NaN 

If need remove columns if both D and C has 0 and another columns has NaNs use DataFrame.all for test if both values are 0 and chain by & for bitwise AND with DataFrame.any for test if at least one value is NaN tested by DataFrame.isna:

m = df[['D','C']].eq(0).all(axis=1) & df.isna().any(axis=1) df1 = df[~m] print (df1) A B C D 0 1.0 0.0 0.0 1.0 1 0.0 NaN NaN 1.0 3 1.0 1.0 NaN NaN 5 0.0 NaN 0.0 1.0 6 0.0 NaN NaN 1.0 7 0.0 1.0 NaN NaN 8 1.0 0.0 1.0 0.0 9 0.0 NaN NaN NaN 

Another alternative without ~ for invert, but all conditions and also & is changed to | for bitwise OR:

m = df[['D','C']].ne(0).any(axis=1) | df.notna().all(axis=1) df1 = df[m] print (df1) A B C D 0 1.0 0.0 0.0 1.0 1 0.0 NaN NaN 1.0 3 1.0 1.0 NaN NaN 5 0.0 NaN 0.0 1.0 6 0.0 NaN NaN 1.0 7 0.0 1.0 NaN NaN 8 1.0 0.0 1.0 0.0 9 0.0 NaN NaN NaN