What is the best way to do iterrows with a subset of a DataFrame?

Let's take the following simple example:

import pandas as pddf = pd.DataFrame({'Product': list('AAAABBAA'),'Quantity': [5,2,5,10,1,5,2,3],'Start' : [DT.datetime(2013,1,1,9,0),DT.datetime(2013,1,1,8,5),DT.datetime(2013,2,5,14,0),DT.datetime(2013,2,5,16,0),DT.datetime(2013,2,8,20,0),                                      DT.datetime(2013,2,8,16,50),DT.datetime(2013,2,8,7,0),DT.datetime(2013,7,4,8,0)]})df = df.set_index(['Start'])

Now I would like to modify a subset of this DataFrame using the itterrows function, e.g.:

for i, row_i in df[df.Product == 'A'].iterrows():row_i['Product'] = 'A1' # actually a more complex calculation

However, the changes do not persist.

Is there any possibility (except a manual lookup using the index 'i') to make persistent changes on the original Dataframe ?

Why do you need iterrows() for this? I think it's always preferrable to use vectorized operations in pandas (or numpy):

df.ix[df['Product'] == 'A', "Product"] = 'A1'