title: Grouping by 30 minutes space in Pandas
body: <p>I have a sequence of one month of data and I wanted the average value at <code>00:00, 00:30, 01:00, ...23:30</code> for the whole month. If this were in an hourly basis I could simply do</p>
  
  <pre><code>df.groupby(df.index.hour).mean()
  </code></pre>
  
  <p>but I have no idea how to do this for a 30 min period. I have tried random things like </p>
  
  <pre><code>df.groupby(df.index.hour*df.index.minute).mean()
  </code></pre>
  
  <p>but none of them has worked. Can this be done in Pandas?</p>
  
  <p>Thank you.</p>
  
  <p><em>EDIT</em>
  Sample data:</p>
  
  <pre><code>2015-06-01 00:00:00     4.474450  137.007017
  2015-06-01 00:30:00     5.661688  138.342549
  2015-06-01 01:00:00     6.142984  139.469381
  2015-06-01 01:30:00     6.245277  140.780341
  2015-06-01 02:00:00     6.368909  141.464176
  2015-06-01 02:30:00     6.535648  143.121590
  ...                          ...         ...
  2015-06-04 21:30:00     6.380301  123.523559
  2015-06-04 22:00:00     6.118872  124.649216
  2015-06-04 22:30:00     6.554864  127.671638
  2015-06-04 23:00:00     7.628708  129.960442
  2015-06-04 23:30:00     8.082754  132.294248
  2015-06-04 00:00:00     7.768733  132.960135
  </code></pre>
  
  <p>Note that the the data spans more than a day, but I am looking for the result in as a <code>24*2</code> length array. Since the <code>00:30</code> data will be the average of the data for all the days in that time and so on.</p>