title: Using pandas to convert string timestamps
body: <p>I've been attempting to use <code>pandas.to_datetime</code> to convert between timestamp formats in my code base, however when provided with a string input sometimes <code>pandas</code> does not seem to extract the UTC offset correctly:</p>
  
  <p>Here is a correct conversion, the UTC offset is correctly captured as reflected in the Timestamp object: </p>
  
  <pre><code>In[76]: pd.to_datetime('2014-04-09T15:29:59.999993-0500', utc=True)
  Out[76]: Timestamp('2014-04-09 20:29:59.999993+0000', tz='UTC')
  </code></pre>
  
  <p>Here is an alternate string representation which is still a valid <a href="http://en.wikipedia.org/wiki/ISO_8601" rel="nofollow">ISO 8601</a> datetime strings but the UTC offset of <code>-0500</code> seems to be ignored:</p>
  
  <pre><code>In[77]: pd.to_datetime('2014-04-09T152959.999993-0500', utc=True)
  Out[77]: Timestamp('2014-04-09 15:29:59.999993+0000', tz='UTC')
  </code></pre>
  
  <p>On the other hand the <a href="http://labix.org/python-dateutil" rel="nofollow">dateutil</a> package handles things fine:</p>
  
  <pre><code>In[78]: dateutil.parser.parse('2014-04-09T152959.999993-0500')
  Out[78]: datetime.datetime(2014, 4, 9, 15, 29, 59, 999993, tzinfo=tzoffset(None, -18000))
  </code></pre>
  
  <p>I could certainly use <code>dateutil</code> but is there some reason that <code>pandas.to_datetime</code> does not handle different ISO date time strings correctly.  Am I doing something wrong here?</p>
  
  <p>Using Python 2.7.6 and <code>pandas 0.13.1</code></p>