Given the following DataFrame:
>>> pd.DataFrame(data=[['a',1],['a',2],['b',3],['b',4],['c',5],['c',6],['d',7],['d',8],['d',9],['e',10]],columns=['key','value'])
key value
0 a 1
1 a 2
2 b 3
3 b 4
4 c 5
5 c 6
6 d 7
7 d 8
8 d 9
9 e 10
I'm looking for a method that will change the structure based on the key value, like so:
a b c d e
0 1 3 5 7 10
1 2 4 6 8 10 <- 10 is duplicated
2 2 4 6 9 10 <- 10 is duplicated
The result row number is as the longest group count (d in the above example) and the missing values are duplicates of the last available value.