title: Parse dates when YYYYMMDD and HH are in separate columns using pandas in Python
body: <p>I have a simple question related with csv files and parsing datetime.</p>
  
  <p>I have a csv file that look like this:</p>
  
  <pre><code>YYYYMMDD, HH,    X
  20110101,  1,   10
  20110101,  2,   20
  20110101,  3,   30
  </code></pre>
  
  <p>I would like to read it using pandas (read_csv) and have it in a dataframe indexed by the datetime. So far I've tried to implement the following:</p>
  
  <pre><code>import pandas as pnd
  pnd.read_csv("..\\file.csv",  parse_dates = True, index_col = [0,1])
  </code></pre>
  
  <p>and the result I get is:</p>
  
  <pre><code>                         X
  YYYYMMDD    HH            
  2011-01-01 2012-07-01   10
             2012-07-02   20
             2012-07-03   30
  </code></pre>
  
  <p>As you see the parse_dates in converting the HH into a different date.</p>
  
  <p>Is there a simple and efficient way to combine properly the column "YYYYMMDD" with the column "HH" in order to have something like this? :</p>
  
  <pre><code>                      X
  Datetime              
  2011-01-01 01:00:00  10
  2011-01-01 02:00:00  20
  2011-01-01 03:00:00  30
  </code></pre>
  
  <p>Thanks in advance for the help.</p>