title: Pandas date_parser function for year:doy:sod format
body: <p>I have a table in which the date and time is given in the format <code>year:day_of_year:seconds_of_day</code> (2nd column). A sample file may be found <a href="https://www.dropbox.com/s/ojsevdn7skho9cm/sample.txt" rel="nofollow noreferrer">here</a></p>
  
  <pre><code>ABCD 15:010:00000 2564.6   4.0  -0.380  0.417  -1.313  0.520
  ABCD 15:010:00300 2564.3   3.7  -0.389  0.396  -1.318  0.503
  ABCD 15:010:00600 2563.9   3.5  -0.397  0.389  -1.324  0.496
  ABCD 15:010:00900 2563.9   3.3  -0.411  0.368  -1.322  0.476
  ABCD 15:010:01200 2563.8   3.0  -0.425  0.361  -1.320  0.466
  ABCD 15:010:01500 2563.9   2.8  -0.432  0.340  -1.312  0.447
  ABCD 15:010:01800 2564.3   2.6  -0.439  0.334  -1.304  0.439
  </code></pre>
  
  <p>I use pandas package to put the above table in a <code>pandas.DataFrame</code>:</p>
  
  <pre><code>names=['Site', 'Epoch', 'TroTot', 'Stdev','TgnTot', 'TgnStd', 'TgeTot', 'TgeStd']
  parser = lambda x: pd.datetime.strptime(x, '%y:%j:%???')
  df = pd.read_csv([FILE][1], 
                   header=None,
                   names=names,
                   delim_whitespace=True,
                   parse_dates=['Epoch'],
                   date_parser=parser)
  </code></pre>
  
  <p>I looked at the documentation but it seems to me that there is no format for the seconds of day. How could I change the parser function to work properly with this format?</p>