title: Average Time difference in pandas
body: <p>I am trying to calculate the average time difference, in hours/minutes/seconds, iterating on a field - in my example, for each different ip address.
  Moreover, a column containing the count of each ip row.</p>
  
  <p>My dataframe looks like:</p>
  
  <pre><code>date                  ipAddress
  2016-08-08 00:39:00   98.249.244.22
  2016-08-08 13:03:00   98.249.244.22
  2016-08-20 21:37:00   98.211.135.179
  2016-08-21 16:11:00   98.211.135.179
  2016-08-21 16:19:00   98.211.135.179
  2016-08-25 01:30:00   98.248.215.244
  </code></pre>
  
  <p>My desired output:</p>
  
  <pre><code>ipAddress         avg_time_diff    count
  98.249.244.22     avg_diff_1         2
  98.211.135.179    avg_diff_2         3
  98.248.215.244    0                  1
  </code></pre>
  
  <p>A reproducicle df:</p>
  
  <pre><code>{u'date': {3233: Timestamp('2016-08-08 00:39:00'),
    3551: Timestamp('2016-08-08 13:03:00'),
    349036: Timestamp('2016-08-20 21:37:00'),
    349040: Timestamp('2016-08-21 16:11:00'),
    349049: Timestamp('2016-08-21 16:19:00'),
    378843: Timestamp('2016-08-25 01:30:00')},
   u'ipAddress': {3233: u'98.249.244.22',
    3551: u'98.249.244.22',
   49036: u'98.211.135.179',
   349040: u'98.211.135.179',
   349049: u'98.211.135.179',
   378843: u'98.248.215.244'}}
  </code></pre>
  
  <p>I have no clue where to start, it tried timediff but I am not sure I've understood how it works and how to iterate over rows as a "window function".
  Thanks in advance</p>