title: python pandas read_csv how to speed up processing timestamps
body: <p>(pandas 0.16.1, Python 2.7.8 Anaconda 2.1.0 (64-bit), Intel Xeon 3.07GHz, Win7 64bit)</p>
  
  <p>I have a csv table of quote data. About 400k rows per day.</p>
  
  <pre><code>sym         time                    bid     ask     bsize asize
  XCME@6EM4   2014.05.07T08:10:02.407 1.3927  1.3928  28    29
  XCME@6EM4   2014.05.07T08:10:02.430 1.3927  1.3928  27    29
  </code></pre>
  
  <p>To read this into Python with pandas</p>
  
  <pre><code>pd.read_csv("quotes.csv", parse_dates = {'idx':[1]}, index_col = 'idx')
  </code></pre>
  
  <p>takes about 40 seconds.</p>
  
  <p>Any idea if this can be made quicker? People have suggested Cython solutions <a href="https://stackoverflow.com/questions/14446744/speed-improvement-on-large-pandas-read-csv-with-datetime-index">in this post</a>, but I wonder if a Python/pandas solution exists?</p>
  
  <p>btw, this below does not parse the dates, a bug?</p>
  
  <pre><code>pd.read_csv("quotes.csv", parse_dates = [1])
  </code></pre>