Update

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    00   AA      05-03   11   AA      06-03   42   AA      07-03   73   AA      08-03   54   BB      05-03   25   BB      06-03   56   BB      07-03   87   BB      08-03   78   CC      05-03   39   CC      06-03   610  CC      07-03   911  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   5BB  05-03   2    06-03   5    07-03   8    08-03   7CC  05-03   3    06-03   6    07-03   9    08-03   1

Use pandas.melt to transform from wide to long:

df = pd.DataFrame({    'date' : ['05/03', '06/03', '07/03', '08/03'],    'AA' : [1, 4, 7, 5],    'BB' : [2, 5, 8, 7],    'CC' : [3, 6, 9, 1]}).set_index('date')df        AA  BB  CCdate            05/03   1   2   306/03   4   5   607/03   7   8   908/03   5   7   1

To convert, we just need to reset the index and then melt:

df = df.reset_index()pd.melt(df, id_vars='date', value_vars=['AA', 'BB', 'CC'])

this is the final result:

    date variable value0   05/03   AA  11   06/03   AA  42   07/03   AA  73   08/03   AA  54   05/03   BB  25   06/03   BB  56   07/03   BB  87   08/03   BB  78   05/03   CC  39   06/03   CC  610  07/03   CC  911  08/03   CC  1

For my dummy test dfs (42 cols, 1k/100k/1M rows) .melt was 8 times faster than .unstack.reset_index()