You can use 2 times cumsum():
# reset val desired_col
#0 0 1 1
#1 0 5 6
#2 0 4 10
#3 1 2 2
#4 1 -1 -1
#5 0 6 5
#6 0 4 9
#7 1 2 2
df['cumsum'] = df['reset'].cumsum()
#cumulative sums of groups to column des
df['des']= df.groupby(['cumsum'])['val'].cumsum()
print df
# reset val desired_col cumsum des
#0 0 1 1 0 1
#1 0 5 6 0 6
#2 0 4 10 0 10
#3 1 2 2 1 2
#4 1 -1 -1 2 -1
#5 0 6 5 2 5
#6 0 4 9 2 9
#7 1 2 2 3 2
#remove columns desired_col and cumsum
df = df.drop(['desired_col', 'cumsum'], axis=1)
print df
# reset val des
#0 0 1 1
#1 0 5 6
#2 0 4 10
#3 1 2 2
#4 1 -1 -1
#5 0 6 5
#6 0 4 9
#7 1 2 2