shifting with re-sampling in time series data

问题

assume that i have this time-series data:

                        A   B
timestamp       
1                       1   2
2                       1   2
3                       1   1
4                       0   1
5                       1   0
6                       0   1
7                       1   0
8                       1   1

i am looking for a re-sample value that would give me specific count of occurrences at least for some frequency

if I would use re sample for the data from 1 to 8 with 2S, i will get different maximum if i would start from 2 to 8 for the same window size (2S)

ds = series.resample( str(tries) +'S').sum()
for shift in range(1,100):
    tries = 1
    series = pd.read_csv("file.csv",index_col='timestamp') [shift:]
    ds = series.resample( str(tries) +'S').sum()
    while ( (ds.A.max + ds.B.max < 4) & (tries < len(ds))):
        ds = series.resample( str(tries) +'S').sum()
        tries = tries + 1
        #other lines

i am looking for performance improvement as it takes prohibitively long to finish for large data

来源：https://stackoverflow.com/questions/45329115/shifting-with-re-sampling-in-time-series-data