title: Speed-improvement on large pandas read_csv with datetime index
body: <p>I have enormous files that look like this:</p>
  
  <p>05/31/2012,15:30:00.029,1306.25,1,E,0,,1306.25</p>
  
  <p>05/31/2012,15:30:00.029,1306.25,8,E,0,,1306.25</p>
  
  <p>I can easily read them using the following:</p>
  
  <pre><code>  pd.read_csv(gzip.open("myfile.gz"), header=None,names=
    ["date","time","price","size","type","zero","empty","last"], parse_dates=[[0,1]])
  </code></pre>
  
  <p>Is there any way to efficiently parse dates like this into pandas timestamps?  If not, is there any guide for writing a cython function that can passed to date_parser= ?</p>
  
  <p>I tried writing my own parser function and it still takes too long for the project I am working on.  </p>