.loc accept row and column selectors simultaneously (as do .ix/.iloc FYI)
This is done in a single pass as well.
In [1]: df = DataFrame(np.random.rand(4,5), columns = list('abcde'))
In [2]: df
Out[2]:
a b c d e
0 0.669701 0.780497 0.955690 0.451573 0.232194
1 0.952762 0.585579 0.890801 0.643251 0.556220
2 0.900713 0.790938 0.952628 0.505775 0.582365
3 0.994205 0.330560 0.286694 0.125061 0.575153
In [5]: df.loc[df['c']>0.5,['a','d']]
Out[5]:
a d
0 0.669701 0.451573
1 0.952762 0.643251
2 0.900713 0.505775
And if you want the values (though this should pass directly to sklearn as is); frames support the array interface
In [6]: df.loc[df['c']>0.5,['a','d']].values
Out[6]:
array([[ 0.66970138, 0.45157274],
[ 0.95276167, 0.64325143],
[ 0.90071271, 0.50577509]])