How to use crosstab/pivot with multi dimensions

Question

I tried using pivot tables to have more than one values in the 'values' field for the pivot_table function but it doesnt work, so im trying to see if i can do it with crosstabs. Here is my code

table=pandas.pivot_table(xl2, values='Applications', rows='Sub-Product',cols='Application Date',aggfunc=numpy.sum)

i get this when i export to csv.

  Sub-Product   11/1/12 11/2/12 11/3/12
    GP            190    207      65
    GPF           1391   1430     1269

In python, after converting it to a pivot table, the dtype is float64() and using

<class 'pandas.core.frame.DataFrame'>

what i ultimately want is this output in csv:

Row Labels  11/1/2012   11/2/2012   11/3/2012
GP          
Acquisitions    164        168          54
Applications    190        207          65
GPF         
Acquisitions    1124       1142         992
Applications    1391       1430         1269

with a code along the lines of this (currently it doesnt work :/ ) :

table=pd.pivot_table(xl2, values=['Acquisitions','Applications'], rows=['Sub-Product'],cols=['Application Date'],aggfunc=np.sum)

But I can only get this:

Sub-Product ('Applications', Timestamp('2012-11-01 00:00:00', tz=None)) ('Applications', Timestamp('2012-11-02 00:00:00', tz=None)) ('Applications', Timestamp('2012-11-03 00:00:00', tz=None))
GP  190 207 65
GPF 1391    1430    1269

Any ideas on how crosstab can help? :S

---

This is the data in the csv file. I'm not sure why i cant get them into the proper dataframe format.

Application Date    Sub-Product Applications    Acquisitions
11/1/12             GP                1            1
11/1/12             GP                1            1
11/1/12             GP                1            1
11/1/12             GP                1            1
11/1/12             GPF               1            1
11/1/12             GPF               1            1
11/1/12             GPF               1            1
11/1/12             GPF               1            1

Answer 1

Looks like you're really close to where you want to be. table.stack(0) will move the first level of the column index to the row index.

In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: df = pd.read_csv(StringIO("""\
   ...: Application-Date    Sub-Product       Applications    Acquisitions
   ...: 11/1/12             GP                1            1
   ...: 11/1/12             GPF               1            1
   ...: 11/2/12             GP                1            1
   ...: 11/2/12             GP                1            1
   ...: 11/2/12             GPF               1            1
   ...: 11/2/12             GPF               1            1
   ...: 11/3/12             GP                1            1
   ...: 11/3/12             GP                1            1
   ...: 11/3/12             GP                1            1
   ...: 11/3/12             GPF               1            1
   ...: 11/3/12             GPF               1            1
   ...: 11/3/12             GPF               1            1
   ...: """), sep='\s+', parse_dates=[0])
In [4]: table = df.pivot_table(values=['Acquisitions', 'Applications'],
   ...:                        rows='Sub-Product',
   ...:                        cols='Application-Date',
   ...:                        aggfunc=sum)
In [5]: table
Out[5]: 
                  Applications                          Acquisitions                        
Application-Date    2012-11-01  2012-11-02  2012-11-03    2012-11-01  2012-11-02  2012-11-03
Sub-Product                                                                                 
GP                           1           2           3             1           2           3
GPF                          1           2           3             1           2           3
In [6]: table.stack(0)
Out[6]: 
Application-Date          2012-11-01  2012-11-02  2012-11-03
Sub-Product                                                 
GP          Applications           1           2           3
            Acquisitions           1           2           3
GPF         Applications           1           2           3
            Acquisitions           1           2           3