Let's assume that I have the following dataframe in pandas:
AA BB CC
date
05/03 1 2 3
06/03 4 5 6
07/03 7 8 9
08/03 5 7 1
and I want to transform it to the following:
AA 05/03 1
AA 06/03 4
AA 07/03 7
AA 08/03 5
BB 05/03 2
BB 06/03 5
BB 07/03 8
BB 08/03 7
CC 05/03 3
CC 06/03 6
CC 07/03 9
CC 08/03 1
How can I do it?
The reason of the transformation from wide to long is that, in the next stage, I would like to merge this dataframe with another one, based on dates and the initial column names (AA, BB, CC).
As George Liu has shown in another answer, pd.melt is the idiomatic, flexible and fast solution to this problem. Do not use unstack for this.
unstack returns a series with a multiindex:
In [38]: df.unstack()
Out[38]:
date
AA 05/03 1
06/03 4
07/03 7
08/03 5
BB 05/03 2
06/03 5
07/03 8
08/03 7
CC 05/03 3
06/03 6
07/03 9
08/03 1
dtype: int64
You can call reset_index on the returning series:
In [39]: df.unstack().reset_index()
Out[39]:
level_0 date 0
0 AA 05-03 1
1 AA 06-03 4
2 AA 07-03 7
3 AA 08-03 5
4 BB 05-03 2
5 BB 06-03 5
6 BB 07-03 8
7 BB 08-03 7
8 CC 05-03 3
9 CC 06-03 6
10 CC 07-03 9
11 CC 08-03 1
Or construct a dataframe with a multiindex:
In [40]: pd.DataFrame(df.unstack())
Out[40]:
0
date
AA 05-03 1
06-03 4
07-03 7
08-03 5
BB 05-03 2
06-03 5
07-03 8
08-03 7
CC 05-03 3
06-03 6
07-03 9
08-03 1