title: pandas filter date time: TypeError: can't compare offset-naive and offset-aware datetimes
body: <p>I have a pandas dataframe:</p>
  
  <pre><code>    name    my_timestamp
  ------------------------------------------
  0   a1      2016-07-28 09:27:07.536963-07:00
  1   a2      2016-07-28 09:27:07.536963-07:00
  2   a3      2016-08-15 13:05:54.924185-07:00
  3   a4      2016-08-30 04:04:18.971667-07:00
  4   a5      2016-03-22 14:36:22.999825-07:00
  5   a6      2016-08-30 04:04:18.971667-07:00
  </code></pre>
  
  <p>I am trying to filter some rows in my pandas data frame like below:</p>
  
  <pre><code>import datetime
  my_df[my_df.my_timestamp &gt; datetime.datetime(2016, 7, 1)]
  </code></pre>
  
  <p>But get the following errors:</p>
  
  <pre><code>TypeErrorTraceback (most recent call last)
  &lt;ipython-input-21-35be746f191d&gt; in &lt;module&gt;()
        1 import datetime
  ----&gt; 2 my_df[my_df.my_timestamp &gt; datetime.datetime(2016, 7, 1)]
  
  /usr/local/lib/python2.7/dist-packages/pandas/core/ops.pyc in wrapper(self, other, axis)
      761                 other = np.asarray(other)
      762 
  --&gt; 763             res = na_op(values, other)
      764             if isscalar(res):
      765                 raise TypeError('Could not compare %s type with Series' %
  
  /usr/local/lib/python2.7/dist-packages/pandas/core/ops.pyc in na_op(x, y)
      681                     result = lib.vec_compare(x, y, op)
      682             else:
  --&gt; 683                 result = lib.scalar_compare(x, y, op)
      684         else:
      685 
  
  pandas/lib.pyx in pandas.lib.scalar_compare (pandas/lib.c:14261)()
  
  TypeError: can't compare offset-naive and offset-aware date times
  </code></pre>
  
  <p>It seems to be the timezone issue. What would be the best way to ignore time-zone here? Thanks!</p>