Groupby Count on user defined time periods pandas

Question

I have a data frame like:

import datetime as dt
import pandas as pd

s = pd.Series(
    range(8),
    pd.to_datetime(
        [
            '20130101 10:34',
            '20130101 10:34:08', 
            '20130101 10:34:08', 
            '20130101 10:34:15', 
            '20130101 10:34:28', 
            '20130101 10:34:54',
            '20130101 10:34:55',
            '20130101 10:35:12'
        ]
    )
)
df = s.to_frame()
df = df.reset_index()
df = df.rename(columns=
               {
                   0         : 'value',
                   'index'   : 'start'
               }
              )
df['ID'] = [1,2,1,2,1,2,1,2]

sec = dt.timedelta(seconds=30)

df['end'] = df['start'].map(lambda t: t + sec)

.

 df


    start               val ID  end
0   2013-01-01 10:34:00 0   1   2013-01-01 10:34:30
1   2013-01-01 10:34:08 1   2   2013-01-01 10:34:38
2   2013-01-01 10:34:08 2   1   2013-01-01 10:34:38
3   2013-01-01 10:34:15 3   2   2013-01-01 10:34:45
4   2013-01-01 10:34:28 4   1   2013-01-01 10:34:58
5   2013-01-01 10:34:54 5   2   2013-01-01 10:35:24
6   2013-01-01 10:34:55 6   1   2013-01-01 10:35:25
7   2013-01-01 10:35:12 7   2   2013-01-01 10:35:42

I have to sum the values of each ID for all rows between the start and end time stamps. To be accurate my result should have this meaning:

p_ = []
#CICLE IS a problem
for row in range(len(df)):
    p_.append(
        #USING LOC IS A PROBLEM
        df.loc[
            (df['start'] >= df['start'][row]) & 
            (df['start'] <= df['end'][row])   &
            (df['ID']    == df['ID'][row])
            ]
        ['value']\
        .sum()
    )
df
    start               val ID  end                 sum_of_values for_ID_in_time_period
0   2013-01-01 10:34:00 0   1   2013-01-01 10:34:30 6
1   2013-01-01 10:34:08 1   2   2013-01-01 10:34:38 4
2   2013-01-01 10:34:08 2   1   2013-01-01 10:34:38 6
3   2013-01-01 10:34:15 3   2   2013-01-01 10:34:45 3
4   2013-01-01 10:34:28 4   1   2013-01-01 10:34:58 10
5   2013-01-01 10:34:54 5   2   2013-01-01 10:35:24 12
6   2013-01-01 10:34:55 6   1   2013-01-01 10:35:25 6
7   2013-01-01 10:35:12 7   2   2013-01-01 10:35:42 7

Instead of the for cycle and the loc I would like to ask for help to transform this problem to some kind of groupby, map solution because my real data set is hardly fits into memory and I have to come up with something faster. I have tried to use:

df.groupby(
    [
        df.start.map(lambda t: t.minute),
        'ID'
    ]
)[['value']]\
.sum()

but this transforms my result something what is not depending on the end column.

          value
start ID    
34    1   12
      2   9
35    2   7