title: Python Pandas: Groupby date, and accessing each group by timestamp
body: <p>I want to groupby timestamp (date) and access each group by timestamp, which looks not working properly.
  It looks like the group keys are strangely indexed with different formats.</p>
  
  <pre><code>df= pd.DataFrame({'DATE' : ['10-Oct-2013', '10-Oct-2013', '10-Oct-2013', '11-Oct-2013', '11-Oct-2013', '11-Oct-2013'],'VAL' : [1,2,3,4,5,6]})
  
  &gt;&gt;&gt; df
            DATE  VAL
  0  10-Oct-2013    1
  1  10-Oct-2013    2
  2  10-Oct-2013    3
  3  11-Oct-2013    4
  4  11-Oct-2013    5
  5  11-Oct-2013    6
  
  
  dfg=df.groupby(df['DATE'].apply(lambda x: pd.to_datetime(x)))
  
  &gt;&gt;&gt; dfg.groups.keys()
  [numpy.datetime64('NaT'), numpy.datetime64('2013-10-10T17:00:00.000000000-0700'), numpy.datetime64('2013-10-09T17:00:00.000000000-0700')]
  
  for d in dfg.groups.keys():
      try:
          print d,dfg.get_group(d).describe()
      except:
          print 'err'
  &gt;&gt;
  NaT err
  2013-10-10T17:00:00.000000000-0700 err
  2013-10-09T17:00:00.000000000-0700 err
  
  rng = pd.to_datetime(pd.date_range('10/10/2013', periods=3, freq='D'))
  
  for d in rng:
      try:
          print d,dfg.get_group(d).describe()
      except:
          print 'err'
  
  2013-10-10 00:00:00 err
  2013-10-11 00:00:00 err
  2013-10-12 00:00:00 err
  </code></pre>