title: Pandas dataframe group by 10 min intervals with different actions on other columns
body: <p>I have a pandas dataframe which includes a timestamp and 71 other columns, something like this:</p>
  <pre><code>              timestamp           |close_price|highest_price|volume| ...
                2018-09-29 00:00:20 |1809       |1811         |  ... |
                2018-09-29 00:00:34 |1823       |1832         |
                2018-09-29 00:00:59 |1832       |1863         |
                2018-09-29 00:01:09 |1800       |1802         |
                2018-09-29 00:01:28 |1832       |1845         |
                .
                .
                .
  </code></pre>
  <p>I want to put the data into 10 min intervals and I want to do separate operations on each column, for example I want the 10 min intervals of <code>close_price</code> column to show the <code>last</code> value of the corresponding range in the real table, or for the <code>highest_price</code> column, I want the <code>max</code> value of the corresponding range, or for <code>volume</code> I want the <code>mean</code> of the values in that range. I already tried</p>
  <pre><code>dataTable = datefram.resample(&quot;10min&quot;).agg({'first_price':'first',
                                         'close_price':'last',
                                         'highest_price': 'max',
                                         'volume':'mean', 
                                          #other attributes...
                                          })
  </code></pre>
  <p>but the result seems to be incorrect.
  Is there any other ways to do what I want to do?
  I will appreciate any comments or thoughts.</p>
  <p>Note that there is no specific pattern in timestamp values. In 1 minute, we can have 0 to 60 rows.</p>