I think you can use loc if you need update two columns to same value:
df1.loc[df1['stream'] == 2, ['feat','another_feat']] = 'aaaa'
print df1
stream feat another_feat
a 1 some_value some_value
b 2 aaaa aaaa
c 2 aaaa aaaa
d 3 some_value some_value
If you need update separate, one option is use:
df1.loc[df1['stream'] == 2, 'feat'] = 10
print df1
stream feat another_feat
a 1 some_value some_value
b 2 10 some_value
c 2 10 some_value
d 3 some_value some_value
Another common option is use numpy.where:
df1['feat'] = np.where(df1['stream'] == 2, 10,20)
print df1
stream feat another_feat
a 1 20 some_value
b 2 10 some_value
c 2 10 some_value
d 3 20 some_value
EDIT: If you need divide all columns without stream where condition is True, use:
print df1
stream feat another_feat
a 1 4 5
b 2 4 5
c 2 2 9
d 3 1 7
#filter columns all without stream
cols = [col for col in df1.columns if col != 'stream']
print cols
['feat', 'another_feat']
df1.loc[df1['stream'] == 2, cols ] = df1 / 2
print df1
stream feat another_feat
a 1 4.0 5.0
b 2 2.0 2.5
c 2 1.0 4.5
d 3 1.0 7.0
If working with multiple conditions is possible use multiple numpy.where
or numpy.select:
df0 = pd.DataFrame({'Col':[5,0,-6]})
df0['New Col1'] = np.where((df0['Col'] > 0), 'Increasing',
np.where((df0['Col'] < 0), 'Decreasing', 'No Change'))
df0['New Col2'] = np.select([df0['Col'] > 0, df0['Col'] < 0],
['Increasing', 'Decreasing'],
default="No Change")
print (df0)
Col New Col1 New Col2
0 5 Increasing Increasing
1 0 No Change No Change
2 -6 Decreasing Decreasing