Update a dataframe in pandas while iterating row by row

Question

I have a pandas data frame that looks like this (its a pretty big one)

           date      exer exp     ifor         mat  
1092  2014-03-17  American   M  528.205  2014-04-19 
1093  2014-03-17  American   M  528.205  2014-04-19 
1094  2014-03-17  American   M  528.205  2014-04-19 
1095  2014-03-17  American   M  528.205  2014-04-19    
1096  2014-03-17  American   M  528.205  2014-05-17 

now I would like to iterate row by row and as I go through each row, the value of ifor in each row can change depending on some conditions and I need to lookup another dataframe.

Now, how do I update this as I iterate. Tried a few things none of them worked.

for i, row in df.iterrows():
    if <something>:
        row['ifor'] = x
    else:
        row['ifor'] = y

    df.ix[i]['ifor'] = x

None of these approaches seem to work. I don't see the values updated in the dataframe.

Answer 1

You can assign values in the loop using df.set_value:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.set_value(i,'ifor',ifor_val)

If you don't need the row values you could simply iterate over the indices of df, but I kept the original for-loop in case you need the row value for something not shown here.

update

df.set_value() has been deprecated since version 0.21.0 you can use df.at() instead:

for i, row in df.iterrows():
    ifor_val = something
    if <condition>:
        ifor_val = something_else
    df.at[i,'ifor'] = ifor_val