title: Aggregation on pandas datetime series only returns as datetime series
body: <p>I have a dataframe like</p>
  
  <pre><code>test = pd.DataFrame({'date': ['2013-10-14 21:46:40', '2013-07-17 02:55:06', '2013-01-28 20:25:17'], 'category': [1, 1, 2]})
  test['date'] = pd.to_datetime(test['date'])
  
       category                  date
  0           1   2013-10-14 21:46:40
  1           1   2013-07-17 02:55:06
  2           2   2013-01-28 20:25:17
  </code></pre>
  
  <p>and I would like to compute some summary statistics for each category, specifically the earliest and latest date as well as the number of items in each category. The obvious way (to me) to do this is:</p>
  
  <pre><code>test.groupby('category')['date'].agg([len, min, max])
  </code></pre>
  
  <p>but when I do this, the <code>len</code> column gets automatically cast as <code>np.datetime64</code>, which I assume is happening because that's the dtype of the original <code>date</code> column:</p>
  
  <pre><code>                                     len                   min                   max
  category                                                                                    
  1          1970-01-01 00:00:00.000000002   2013-07-17 02:55:06   2013-10-14 21:46:40
  2          1970-01-01 00:00:00.000000001   2013-01-28 20:25:17   2013-01-28 20:25:17
  </code></pre>
  
  <p>I could go back and reconvert this <code>len</code> column to nanoseconds since GMT epoch, but that is pretty ugly and I feel like there must be a better way. Any ideas?</p>