You can do the same with .ix, like this:
In [1]: df = pd.DataFrame(np.random.randn(5,4), columns=list('abcd'))
In [2]: df
Out[2]:
a b c d
0 -0.323772 0.839542 0.173414 -1.341793
1 -1.001287 0.676910 0.465536 0.229544
2 0.963484 -0.905302 -0.435821 1.934512
3 0.266113 -0.034305 -0.110272 -0.720599
4 -0.522134 -0.913792 1.862832 0.314315
In [3]: df.ix[df.a>0, ['b','c']] = 0
In [4]: df
Out[4]:
a b c d
0 -0.323772 0.839542 0.173414 -1.341793
1 -1.001287 0.676910 0.465536 0.229544
2 0.963484 0.000000 0.000000 1.934512
3 0.266113 0.000000 0.000000 -0.720599
4 -0.522134 -0.913792 1.862832 0.314315
EDIT
After the extra information, the following will return all columns - where some condition is met - with halved values:
>> condition = df.a > 0
>> df[condition][[i for i in df.columns.values if i not in ['a']]].apply(lambda x: x/2)
I hope this helps!