A part of the answer can be found here (How to select rows from a DataFrame based on column values?), however it's only for one column. I'm wondering how to apply it for paired values in multiple columns (two in this case).
I have a data frame df where the filtering columns are B and C (NaN represents empty cells):
A B C D
0 1 Blue Green 4
1 2 Blue Green 6
2 3 Blue Green 2
3 4 Blue NaN 6
4 5 Blue NaN 9
5 6 NaN Green 8
6 7 Blue Green 8
7 8 NaN NaN 9
8 9 NaN Green 1
9 10 NaN Green 2
I would only like to keep the rows with respect to B and C for which B='Blue' and C='Green', all the other rows can be dropped, ideally producing:
A B C D
0 1 Blue Green 4
1 2 Blue Green 6
2 3 Blue Green 2
6 7 Blue Green 8
Any ideas?
Code for the dataframe:
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": [1,2,3,4,5,6,7,8,9,10], "B": ['Blue', 'Blue', 'Blue', 'Blue','Blue', np.nan,
'Blue', np.nan, np.nan, np.nan], "C": ['Green', 'Green', 'Green', np.nan, np.nan, 'Green', 'Green',
np.nan, 'Green', 'Green'], "D": [4,6,2,6,9,8,8,9,1,2]})
print(df)
