This will be much faster that using the read_csv date parser to do this conversion.

In [6]: data = """DATAH   DATE    TIME    SECONDS NANOSECONDS D
   ...: DATA    2012-06-04  23:49:15    1338853755  700000000   0.00855577
   ...: DATA    2012-06-04  23:49:15    1338853755  800000000   0.00805482
   ...: DATA    2012-06-04  23:49:15    1338853755  900000000   -0.00537284
   ...: DATA    2012-06-04  23:49:16    1338853756  0   -0.0239447"""

In [7]: df = read_csv(StringIO(data),sep='\s+')

In [8]: df
Out[8]: 
  DATAH        DATE      TIME     SECONDS  NANOSECONDS         D
0  DATA  2012-06-04  23:49:15  1338853755    700000000  0.008556
1  DATA  2012-06-04  23:49:15  1338853755    800000000  0.008055
2  DATA  2012-06-04  23:49:15  1338853755    900000000 -0.005373
3  DATA  2012-06-04  23:49:16  1338853756            0 -0.023945

[4 rows x 6 columns]

In [9]: df.dtypes
Out[9]: 
DATAH           object
DATE            object
TIME            object
SECONDS          int64
NANOSECONDS      int64
D              float64
dtype: object

In [13]: pd.to_datetime(df['SECONDS']+df['NANOSECONDS'].astype(float)/1e9, unit='s')
Out[13]: 
0   2012-06-04 23:49:15.700000
1   2012-06-04 23:49:15.800000
2   2012-06-04 23:49:15.900000
3          2012-06-04 23:49:16
dtype: datetime64[ns]


Parse date, time and nanoseconds as datetime objects using pandas
Does anyone have a very clear step-by-step guide for implementing OAuth2 (preferably in Kohana 3.2, but I'll take whatever I can get)?

I would like it to include the tables I need to create, etc.  (Hard to implement something without knowing the table structure.)

UPDATE:

I completely forgot to mention that I'm looking for a Provider implementation!  Thanks! (and sorry).
