One way to do this is to insert a dummy column with the sums in order to sort:
In [10]: sum_B_over_A = df.groupby('A').sum().B
In [11]: sum_B_over_A
Out[11]:
A
bar 0.253652
baz -2.829711
foo 0.551376
Name: B
in [12]: df['sum_B_over_A'] = df.A.apply(sum_B_over_A.get_value)
In [13]: df
Out[13]:
A B C sum_B_over_A
0 foo 1.624345 False 0.551376
1 bar -0.611756 True 0.253652
2 baz -0.528172 False -2.829711
3 foo -1.072969 True 0.551376
4 bar 0.865408 False 0.253652
5 baz -2.301539 True -2.829711
In [14]: df.sort(['sum_B_over_A', 'A', 'B'])
Out[14]:
A B C sum_B_over_A
5 baz -2.301539 True -2.829711
2 baz -0.528172 False -2.829711
1 bar -0.611756 True 0.253652
4 bar 0.865408 False 0.253652
3 foo -1.072969 True 0.551376
0 foo 1.624345 False 0.551376
and maybe you would drop the dummy row:
In [15]: df.sort(['sum_B_over_A', 'A', 'B']).drop('sum_B_over_A', axis=1)
Out[15]:
A B C
5 baz -2.301539 True
2 baz -0.528172 False
1 bar -0.611756 True
4 bar 0.865408 False
3 foo -1.072969 True
0 foo 1.624345 False