Say I have the following dataframe:

table

What is the most efficient way to update the values of the columns feat and another_feat where the stream is number 2?

Is this it?

for index, row in df.iterrows():
    if df1.loc[index,'stream'] == 2:
       # do something

UPDATE:
What to do if I have more than a 100 columns? I don’t want to explicitly name the columns that I want to update. I want to divide the value of each column by 2 (except for the stream column).

So to be clear what my goal is:

Dividing all values by 2 of all rows that have stream 2, but not changing the stream column

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

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)