Converting year and day of year into datetime index in pandas

In pd.to_datetime() you may specify the format and origin date as:

pd.to_datetime(df['doy'], unit='D', origin=pd.Timestamp(df['year']))

This method does all the hard work for you.

You can use the date specifier %j to extract the day of year. So combine the two columns, shift the year, and convert to datetime!

pd.to_datetime(df['year'] * 1000 + df['doy'], format='%Y%j')

returns

0   2000-02-18
1   2000-03-05
2   2000-03-21
3   2001-04-07
4   2001-04-23
5   2001-05-09
6   2001-05-25
7   2001-06-10
dtype: datetime64[ns]

You can use NumPy datetime64/timedelta64 arithmetic to find the desired dates:

In [97]: (np.asarray(df['year'], dtype='datetime64[Y]')-1970)+(np.asarray(df['doy'], dtype='timedelta64[D]')-1)
Out[97]: 
array(['2000-02-18', '2000-03-05', '2000-03-21', '2001-04-07',
       '2001-04-23', '2001-05-09', '2001-05-25', '2001-06-10'], dtype='datetime64[D]')

Since composing dates given various parts of dates (e.g. years, months, days, weeks, hours, etc.) is a common problem, here is a utility function to make it easier:

def compose_date(years, months=1, days=1, weeks=None, hours=None, minutes=None,
                 seconds=None, milliseconds=None, microseconds=None, nanoseconds=None):
    years = np.asarray(years) - 1970
    months = np.asarray(months) - 1
    days = np.asarray(days) - 1
    types = ('<M8[Y]', '<m8[M]', '<m8[D]', '<m8[W]', '<m8[h]',
             '<m8[m]', '<m8[s]', '<m8[ms]', '<m8[us]', '<m8[ns]')
    vals = (years, months, days, weeks, hours, minutes, seconds,
            milliseconds, microseconds, nanoseconds)
    return sum(np.asarray(v, dtype=t) for t, v in zip(types, vals)
               if v is not None)

df = pd.DataFrame({'doy': [49, 65, 81, 97, 113, 129, 145, 161],
                   'year': [2000, 2000, 2000, 2001, 2001, 2001, 2001, 2001]})

df.index = compose_date(df['year'], days=df['doy'])

yields

            doy  year
2000-02-18   49  2000
2000-03-05   65  2000
2000-03-21   81  2000
2001-04-07   97  2001
2001-04-23  113  2001
2001-05-09  129  2001
2001-05-25  145  2001
2001-06-10  161  2001