Reputation: 323
I'm sure this is fairly easy to do but I can't figure it out. I'm coding in python and using pandas to work on dataframes. My dataframe looks like this:
a b c
1 10 20
1 30 40
1 60 70
1 80 100
2 10 20
2 60 70
2 80 100
And I would like to transform it like that:
a b1 c1 b2 c2 b3 c3 b4 c4
1 10 20 30 40 60 70 80 100
2 10 20 60 70 80 100 NA NA
Basically for every rows where the value in a
is the same, take values in b
and c
and transform them into new columns, so that I end up with only one row for every value in a
.
I hope I'm clear enough, if not don't hesitate to tell me. Thanks in advance for any help provided !
Florian.
Upvotes: 1
Views: 1087
Reputation: 323226
We need create a help key here, by using cumcount
s=df.assign(key=df.groupby('a').cumcount()+1).set_index(['a','key']).stack().unstack([1,2])
s.columns=s.columns.map('{0[1]}{0[0]}'.format)
s
Out[396]:
b1 c1 b2 c2 b3 c3 b4 c4
a
1 10.0 20.0 30.0 40.0 60.0 70.0 80.0 100.0
2 10.0 20.0 60.0 70.0 80.0 100.0 NaN NaN
Upvotes: 2