Let's say I have the following data sample:

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.yeardfdate        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

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 :

    week    revenue
0   1       8
1   2       33
2   52      22

I first thought of using the week number given by timestamp.week.
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 week_number would in that case sum both the revenue at the very beginning of the year, and those at the end of the year.

When you convert using dt.week , it is ISO week date.

You can using strftime

df.groupby(df.date.dt.strftime('%W')).revenue.sum()
Out[588]: 
date
00     8
01    33
52    22
Name: revenue, dtype: int64