python - How to use crosstab/pivot with multi dimensions -

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i tried using pivot tables have more 1 values in 'values' field pivot_table function doesnt work, im trying see if can crosstabs. here code

table=pandas.pivot_table(xl2, values='applications', rows='sub-product',cols='application date',aggfunc=numpy.sum)

i when export csv.

  sub-product   11/1/12 11/2/12 11/3/12     gp            190    207      65     gpf           1391   1430     1269

in python, after converting pivot table, dtype float64() , using

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

what want 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 code along lines of (currently doesnt work :/ ) :

table=pd.pivot_table(xl2, values=['acquisitions','applications'], rows=['sub-product'],cols=['application date'],aggfunc=np.sum)

but can 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 data in csv file. i'm not sure why cant them 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

looks you're close want be. table.stack(0) move first level of column index row index.

in [1]: import pandas pd in [2]: 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

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