title: How to use crosstab/pivot with multi dimensions
body: <p>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</p>
  
  <pre><code>table=pandas.pivot_table(xl2, values='Applications', rows='Sub-Product',cols='Application Date',aggfunc=numpy.sum)
  </code></pre>
  
  <p>i get this when i export to csv.</p>
  
  <pre><code>  Sub-Product   11/1/12 11/2/12 11/3/12
      GP            190    207      65
      GPF           1391   1430     1269
  </code></pre>
  
  <p>In python, after converting it to a pivot table, the dtype is float64() and using</p>
  
  <pre><code>&lt;class 'pandas.core.frame.DataFrame'&gt;
  </code></pre>
  
  <p>what i ultimately want is this output in csv:</p>
  
  <pre><code>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
  </code></pre>
  
  <p>with a code along the lines of this (currently it doesnt work :/ ) :</p>
  
  <pre><code>table=pd.pivot_table(xl2, values=['Acquisitions','Applications'], rows=['Sub-Product'],cols=['Application Date'],aggfunc=np.sum)
  </code></pre>
  
  <p>But I can only get this:</p>
  
  <pre><code>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
  </code></pre>
  
  <p>Any ideas on how crosstab can help? :S</p>
  
  <hr>
  
  <p>This is the data in the csv file. I'm not sure why i cant get them into the proper dataframe format.</p>
  
  <pre><code>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
  </code></pre>