title: Pandas timestamp doesn't play nicely with numpy datetime?
body: <p>I'm just beginning to use pandas 0.9 and am seeing unexpected behavior with pandas timestamps. After setting an index with a datetime, the timeindex doesn't seem to correctly convert to anything else. I'm probably not using it correctly, so please set me straight: </p>
  
  <pre><code>from pandas import *
  import datetime
  
  version.version
  # 0.9.1
  
  np.version.version
  # 1.6.2
  
  ndx = ['a','b','b']
  date = [datetime.datetime(2013, 2, 16, 15, 0), datetime.datetime(2013, 2, 16, 11, 0),datetime.datetime(2013, 2, 16, 2, 0)]
  vals = [1,2,3,]
  
  df = DataFrame({'ndx':ndx,'date':date,'vals':vals})
  df2=df.groupby(['ndx','date']).sum()
  
  df2.index.get_level_values('date')
  # array([1970-01-16 143:00:00, 1970-01-16 130:00:00, 1970-01-16 139:00:00], dtype=datetime64[ns])
  
  df.set_index([ndx,date]).reset_index()['level_1'].unique() # fetch from index
  # array([1970-01-16 143:00:00, 1970-01-16 139:00:00, 1970-01-16 130:00:00], dtype=datetime64[ns])
  
  df.set_index([ndx,date]).reset_index()['date'].unique() # fetch from column
  # array([2013-02-16 15:00:00, 2013-02-16 11:00:00, 2013-02-16 02:00:00], dtype=object)
  </code></pre>
  
  <p>I would wouldn't expect anything with 1970 as a result of these operations. Thoughts? </p>