title: Pandas groupby week given a datetime column
body: <p>Let's say I have the following data sample:</p>
  
  <pre class="lang-py prettyprint-override"><code>df = pd.DataFrame({'date':['2011-01-01','2011-01-02',
                         '2011-01-03','2011-01-04','2011-01-05',
                         '2011-01-06','2011-01-07','2011-01-08',
                         '2011-01-09','2011-12-30','2011-12-31'],
                     'revenue':[5,3,2,
                                10,12,2,
                                1,0,6,10,12]})
  
  # Let's format the date and add the week number and year
  df['date'] = pd.to_datetime(df['date'],format='%Y-%m-%d')
  df['week_number'] = df['date'].dt.week
  df['year'] = df['date'].dt.year
  
  df
  
          date        revenue     week_of_year    year
  0       2011-01-01  5           52              2011
  1       2011-01-02  3           52              2011
  2       2011-01-03  2           1               2011
  3       2011-01-04  10          1               2011
  4       2011-01-05  12          1               2011
  5       2011-01-06  2           1               2011
  6       2011-01-07  1           1               2011
  7       2011-01-08  0           1               2011
  8       2011-01-09  6           1               2011
  9       2011-12-30  10          52              2011
  10      2011-12-31  12          52              2011
  </code></pre>
  
  <p>I would like to compute the revenue per week, in order to later plot the results, and analyze the time series. The expected output would then be something like that :</p>
  
  <pre class="lang-py prettyprint-override"><code>    week    revenue
  0   1       8
  1   2       33
  2   52      22
  </code></pre>
  
  <p>I first thought of using the week number given by <code>timestamp.week</code>. <br/>However, I can't figure out how to deal with the ISO week number definition for the week preceeding week number 1. I am a bit confused, since grouping by <code>week_number</code> would in that case sum both the revenue at the very beginning of the year, and those at the end of the year.</p>