title: Pandas event dataframe (start stop) to regular time series
body: <p>I am dealing with a dataset of events. Every row has a start time, an end time and a value.</p>
  
  <p>It looks like this one:</p>
  
  <pre><code>df = pd.DataFrame({'start': ['2015-01-05 12:21:00', '2015-01-05 18:01:23', '2015-01-05 23:11:01'], 'end': ['2015-01-05 13:18:45', '2015-01-05 21:03:51', '2015-01-05 12:08:11'], 'value': [3, 4, 5]})
  
      end start   value
  0   2015-01-07 11:18:45 2015-01-07 11:35:00 3
  1   2015-01-07 12:08:11 2015-01-07 23:11:01 5
  2   2015-01-07 17:03:51 2015-01-07 18:01:23 4
  </code></pre>
  
  <p>I need to resample it in a 10 minutes regular time series like the following:</p>
  
  <pre><code>    time values
  1   2015-01-07 11:01:00  NULL
  2   2015-01-07 11:11:00  3
  3   2015-01-07 11:21:00  3
  4   2015-01-07 11:31:00  3
  5   2015-01-07 11:41:00  NULL
  ...
  ..  2015-01-07 12:11:00  5
  ...
  ..  2015-01-07 17:01:00  5,4
  ...
  </code></pre>
  
  <p>What is the most efficient way to deal with this resampling? I also highlighted the possibility of multiple events with the last value "5,4", what could be the best way to deal with it?</p>
  
  <p>Thanks!</p>