Update row values where certain condition is met in pandas

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_feata       1  some_value   some_valueb       2        aaaa         aaaac       2        aaaa         aaaad       3  some_value   some_value

If you need update separate, one option is use:

df1.loc[df1['stream'] == 2, 'feat'] = 10print df1   stream        feat another_feata       1  some_value   some_valueb       2          10   some_valuec       2          10   some_valued       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_feata       1    20   some_valueb       2    10   some_valuec       2    10   some_valued       3    20   some_value

EDIT: If you need divide all columns without stream where condition is True, use:

print df1   stream  feat  another_feata       1     4             5b       2     4             5c       2     2             9d       3     1             7#filter columns all without streamcols = [col for col in df1.columns if col != 'stream']print cols['feat', 'another_feat']df1.loc[df1['stream'] == 2, cols ] = df1 / 2print df1   stream  feat  another_feata       1   4.0           5.0b       2   2.0           2.5c       2   1.0           4.5d       3   1.0           7.0

If working with multiple conditions is possible use multiple numpy.whereor 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 Col20    5  Increasing  Increasing1    0   No Change   No Change2   -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]: dfOut[2]:           a         b         c         d0 -0.323772  0.839542  0.173414 -1.3417931 -1.001287  0.676910  0.465536  0.2295442  0.963484 -0.905302 -0.435821  1.9345123  0.266113 -0.034305 -0.110272 -0.7205994 -0.522134 -0.913792  1.862832  0.314315In [3]: df.ix[df.a>0, ['b','c']] = 0In [4]: dfOut[4]:           a         b         c         d0 -0.323772  0.839542  0.173414 -1.3417931 -1.001287  0.676910  0.465536  0.2295442  0.963484  0.000000  0.000000  1.9345123  0.266113  0.000000  0.000000 -0.7205994 -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)