title: define aggfunc for each values column in pandas pivot table
body: <p>Was trying to generate a pivot table with multiple "values" columns. I know I can use aggfunc to aggregate values the way I want to, but what if I don't want to sum or avg both columns but instead I want sum of one column while mean of the other one. So is it possible to do so using pandas?</p>
  
  <pre><code>df = pd.DataFrame({
            'A' : ['one', 'one', 'two', 'three'] * 6,
            'B' : ['A', 'B', 'C'] * 8,
            'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
            'D' : np.random.randn(24),
            'E' : np.random.randn(24)
  })
  </code></pre>
  
  <p>Now this will get a pivot table with sum:</p>
  
  <pre><code>pd.pivot_table(df, values=['D','E'], rows=['B'], aggfunc=np.sum)
  </code></pre>
  
  <p>And this for mean:</p>
  
  <pre><code>pd.pivot_table(df, values=['D','E'], rows=['B'], aggfunc=np.mean)
  </code></pre>
  
  <p>How can I get sum for <code>D</code> and mean for <code>E</code>?</p>
  
  <p>Hope my question is clear enough.</p>