convert column into headings for dataframe

Question

I have this df (exceprt shown below) which has a column called GRP with data in other columns. What I'd like to do is have the other columns split out by the value of Grp w9th the Grp value as a column heading.

Original DF

>>> xx
        x1              Grp
2    3.670     8ZD (S00K49)
3    3.659     8ZD (S00K49)
4    3.576     8ZD (S00K49)
5    3.509     8ZD (S00K49)
10   3.879  MPN 603 (Pos 1)
11   3.816  MPN 603 (Pos 1)
12   3.881  MPN 603 (Pos 1)
17   3.813  MPN 604 (Pos 1)
20   3.670     8ZD (S00K49)
21   3.612     8ZD (S00K49)
36   3.774  MPN 603 (Pos 1)
37   3.752  MPN 603 (Pos 1)
38   3.667  MPN 603 (Pos 1)
39   3.717  MPN 603 (Pos 1)
40   3.730  MPN 604 (Pos 1)
41   3.771  MPN 604 (Pos 1)
42   3.621  MPN 604 (Pos 1)

Desired Result:

>>> yy

     8ZD (S00K49)  MPN 603 (Pos 1) MPN 604 (Pos1) 
2    3.670         3.879           3.881    
3    3.659         3.816           3.813 
4    3.576         NaN             NaN 
5    3.509         NaN             NaN 

I've been unable to find how to do this either online or in the documentation. Experimented a bit with the stack and unstack methods but never got what I was looking for that way. ANy tips much appreaciated.

Answer 1

If you groupby by Grp , you can add an "index" which is a simple cumulative count of the number of items in each group:

In [18]: df['index'] = df.groupby(['Grp']).cumcount()

In [19]: df
Out[19]: 
       x1              Grp  index
2   3.670     8ZD (S00K49)      0
3   3.659     8ZD (S00K49)      1
4   3.576     8ZD (S00K49)      2
5   3.509     8ZD (S00K49)      3
10  3.879  MPN 603 (Pos 1)      0
11  3.816  MPN 603 (Pos 1)      1
12  3.881  MPN 603 (Pos 1)      2
17  3.813  MPN 604 (Pos 1)      0
20  3.670     8ZD (S00K49)      4
21  3.612     8ZD (S00K49)      5
36  3.774  MPN 603 (Pos 1)      3
37  3.752  MPN 603 (Pos 1)      4
38  3.667  MPN 603 (Pos 1)      5
39  3.717  MPN 603 (Pos 1)      6
40  3.730  MPN 604 (Pos 1)      1
41  3.771  MPN 604 (Pos 1)      2
42  3.621  MPN 604 (Pos 1)      3

In [23]: result = df.pivot(index='index', 
                           columns='Grp', values='x1')

yields

In[24]: result
Out[24]:
Grp    8ZD (S00K49)  MPN 603 (Pos 1)  MPN 604 (Pos 1)
index                                                
0             3.670            3.879            3.813
1             3.659            3.816            3.730
2             3.576            3.881            3.771
3             3.509            3.774            3.621
4             3.670            3.752              NaN
5             3.612            3.667              NaN
6               NaN            3.717              NaN