I have 2 dataframes, both have a key column which could have duplicates, but the dataframes mostly have the same duplicated keys. I'd like to merge these dataframes on that key, but in such a way that when both have the same duplicate those duplicates are merged respectively. In addition if one dataframe has more duplicates of a key than the other, I'd like it's values to be filled as NaN. For example:
df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K2', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']},
columns=['key', 'A'])
df2 = pd.DataFrame({'B': ['B0', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6'],
'key': ['K0', 'K1', 'K2', 'K2', 'K3', 'K3', 'K4']},
columns=['key', 'B'])
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K2 A3
4 K2 A4
5 K3 A5
key B
0 K0 B0
1 K1 B1
2 K2 B2
3 K2 B3
4 K3 B4
5 K3 B5
6 K4 B6
I'm trying to get the following output
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K2 A3 B3
6 K2 A4 NaN
8 K3 A5 B4
9 K3 NaN B5
10 K4 NaN B6
So basically, I'd like to treat the duplicated K2 keys as K2_1, K2_2, ... and then do the how='outer' merge on the dataframes.
Any ideas how I can accomplish this?
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