title: Pandas read_csv: parsing time field correctly
body: <p>I have the following raw data,</p>
  
  <pre><code>TranID,TranDate,TranTime,TranAmt
  A123456,20160427,02:18,9999.53
  B123457,20160426,02:48,26070.33
  C123458,20160425,03:18,13779.56
  A123459,20160424,03:18,18157.26
  B123460,20160423,04:18,215868.15
  C123461,20160422,04:18,23695.25
  A123462,20160421,05:18,57
  B123463,20160420,05:18,64594.24
  C123464,20160419,06:18,47890.91
  A123465,20160427,06:18,14119.74
  B123466,20160426,07:18,2649.6
  C123467,20160425,07:18,16757.38
  A123468,20160424,08:18,8864.78
  B123469,20160423,08:18,26254.69
  C123470,20160422,09:18,13206.98
  A123471,20160421,09:18,15872.45
  B123472,20160420,10:18,197621.18
  C123473,20160419,10:18,21048.72
  </code></pre>
  
  <p>and I tried importing the raw data using pd read_csv,</p>
  
  <p><strong>Try1</strong></p>
  
  <pre><code>import numpy as np
  import pandas as pd
  
  df = pd.read_csv('MyTest.csv', sep=',', header=0, parse_dates=['TranDate'],
                       usecols=['TranID','TranDate','TranTime','TranAmt'],
                       engine='python')
  print(df.dtypes)
  df[:5]
  </code></pre>
  
  <p>Output1</p>
  
  <pre><code>TranID              object
  TranDate    datetime64[ns]
  TranTime            object
  TranAmt            float64
  dtype: object
  Out[12]:
  TranID  TranDate    TranTime    TranAmt
  0   A123456 2016-04-27  02:18   9999.53
  1   B123457 2016-04-26  02:48   26070.33
  2   C123458 2016-04-25  03:18   13779.56
  3   A123459 2016-04-24  03:18   18157.26
  4   B123460 2016-04-23  04:18   215868.15
  </code></pre>
  
  <p><strong>Try2</strong></p>
  
  <pre><code>import numpy as np
  import pandas as pd
  
  df = pd.read_csv('MyTest.csv', sep=',', header=0, parse_dates=['TranDate', 'TranTime'],
                   usecols=['TranID','TranDate','TranTime','TranAmt'],
                   engine='python')
  print(df.dtypes)
  df[:5]
  </code></pre>
  
  <p>Output2</p>
  
  <pre><code>TranID              object
  TranDate    datetime64[ns]
  TranTime    datetime64[ns]
  TranAmt            float64
  dtype: object
  Out[13]:
  TranID  TranDate    TranTime    TranAmt
  0   A123456 2016-04-27  2016-04-27 02:18:00 9999.53
  1   B123457 2016-04-26  2016-04-27 02:48:00 26070.33
  2   C123458 2016-04-25  2016-04-27 03:18:00 13779.56
  3   A123459 2016-04-24  2016-04-27 03:18:00 18157.26
  4   B123460 2016-04-23  2016-04-27 04:18:00 215868.15
  </code></pre>
  
  <p>My confusions are with the TranTime column.  In Try1, it is displayed correctly but dtype is object.  In Try2, pd added current date to the time and the dtype is datetime.  </p>
  
  <p>I want this TranTime column to be treated as Time and want to perform aggregations using pd's groupby or pivot_table.
  If I use Try1 method, does the object dtype affect my aggregations?
  If I use Try2 method, do I need to strip the Date part out in order to use the Time part?</p>
  
  <p>I am proficient in SAS where SAS has date, time and datetime informats and formats where the underlying data type is just numeric.  Hence my confusion with Python's object and datetime dtypes.</p>
  
  <p>Thanks,
  Lobbie</p>