TimeGrouper frequency mismatch
Hi everybody,

I have the following DataFrame:

df = pd.DataFrame({
'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
'Quantity': [1,3,5,8,9,3],
'Date' : [
    DT.datetime(2013,9,1,13,0),
    DT.datetime(2013,9,1,13,5),
    DT.datetime(2013,10,1,20,0),
    DT.datetime(2013,10,3,10,0),
    DT.datetime(2013,12,2,12,0),  
    DT.datetime(2013,12,2,14,0),
    ]})

df = df.set_index('Date', drop=False)

With the following function:

def f(frame):
    cum_sum = 0
    if (len(frame) > 0):
        cum_sum = frame.Quantity.sum()
    frame['cum_sum'] = cum_sum
    return frame

x1 = df.groupby(pd.TimeGrouper('20M')).apply(f)

I get a wrong output for the first two rows in x1. Instead of 25, 4 is computed as cum_sum. However, if I use 'A' as a yearly quantifier in the TimeGrouper function the cum_sum of the first two rows is computed correctly?
