title: Parse dates when year month day and hour are in separate columns using pandas in python
body: <p>After reading 
  <a href="https://stackoverflow.com/questions/11615504/parse-dates-when-yyyymmdd-and-hh-are-in-separate-columns-using-pandas-in-python">Parse dates when YYYYMMDD and HH are in separate columns using pandas in Python</a>
  and
  <a href="https://stackoverflow.com/questions/12269528/using-python-pandas-to-parse-csv-with-date-in-format-year-day-hour-min-sec">Using python pandas to parse CSV with date in format Year, Day, Hour, Min, Sec</a></p>
  
  <p>I still am not able to parse dates with separated columns for year, month, day and hour. My data looks like this (zeroth column is ID, first is year, second is month, third is day, fourth is hour and fifth is value)</p>
  
  <pre><code>50136   2011    1   1   21  9792    
  50136   2011    1   1   22  9794    
  50136   2011    1   1   23  9796    
  50136   2011    1   1   0   9798    
  50136   2011    1   1   1   9799    
  50136   2011    1   1   2   9802
  </code></pre>
  
  <p>I've tried following:
  <code>df = pd.read_csv(file, parse_dates = {'date': [1, 2, 3, 4]},  , index_col='date')</code>, but then I get index not as timestamp but as unicode(?)</p>
  
  <pre><code>In  [17]: print df.head()
  Out [17]:
                   0     5
  date                    
  2011 1 1 21  50136  9792
  2011 1 1 22  50136  9794
  2011 1 1 23  50136  9796
  2011 1 1 0   50136  9798
  2011 1 1 1   50136  9799
  
  In  [18]: print df.index
  Out [18]:
  Index([u'2011 1 1 21', u'2011 1 1 22', u'2011 1 1 23', u'2011 1 1 0', u'2011 1 1 1', u'2011 1 1 2'], dtype=object)
  </code></pre>
  
  <p>I'm obviously doing something wrong, but I can't figure it out. Any advise is really appreciated.</p>