Krdytkyu

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