title: Pandas rolling median for duplicate time series data
body: <p>I see that Pandas does not allow <a href="https://github.com/pydata/pandas/issues/643" rel="nofollow noreferrer">duplicate time series indexes</a> yet (<a href="https://github.com/pydata/pandas/issues/643" rel="nofollow noreferrer">https://github.com/pydata/pandas/issues/643</a>), but will be added soon.  I am wondering if there is a good way to apply rolling window means to a dataset with duplicate times by a multi-index tag/column</p>
  
  <p>Basically I have a csv of non-ordered events that consist of epochtime,  hierarchical tags (tag1, tag2), and time taken. A small sample:</p>
  
  <pre><code> epochTimeMS,event,tag,timeTakenMS
   1331782842801,event1,tag1,16
   1331782841535,event1,tag2,1278
   1331782842801,event1,tag1,17
   1331782842381,event2,tag1,436
  </code></pre>
  
  <p>What I want to do is build and graph rolling means with varying ms windows, by event and event+tag. This seems like it should be accomplished in Pandas, but not sure if I will need to wait until the duplicate time-series indexes first.  Any thoughts on hacking this in place now?</p>