title: Panda Dataframe Resampling based on column criteria
body: <p>I want to resample a dataframe if cell in another column matches my criteria</p>
  
  <pre><code>df = pd.DataFrame({
          'timestamp': [
              '2013-03-01 08:01:00', '2013-03-01 08:02:00',
              '2013-03-01 08:03:00', '2013-03-01 08:04:00',
              '2013-03-01 08:05:00', '2013-03-01 08:06:00'
          ],
          'Kind': [
              'A', 'B', 'A', 'B', 'A', 'B'
          ],
          'Values': [1, 1.5, 2, 3, 5, 3]
      })
  </code></pre>
  
  <p>For every timestamp, I may have 2-10 kinds, and I want to resample correctly without producing <code>NaN</code>. Currently I resample on the entire dataframe using below code and get <code>NaNs</code>. I think it's due to I have multiple entries for certain timestamps.</p>
  
  <pre><code>df.set_index('timestamp').resample('5Min').mean()
  </code></pre>
  
  <p>One method is to create different dataframes for every kind, resample every dataframe, and join the resulting dataframes. I'd like to find out if there's any simple way of doing it.</p>