Grouping and auto increment based on columns in pandas

Question

i have a pandas frame that looks like this:

Is there a way to add the numbers in the last column without having to iterate through the data frame?

I was playing with the results of Grouping and auto incrementing group id in pandas but haven't made it work for my purposes

Here is the code to produce the dataframe

import pandas as pd
columns = ['Product','SubProd', 'NeedThis']
Index=['4/20/2012','4/27/2012','5/4/2012','5/11/2012','5/18/2012','4/20/2012',
'4/27/2012','5/4/2012','5/11/2012','5/18/2012','5/25/2012','10/31/2014','11/7/2014',
'11/14/2014','11/21/2014','11/28/2014']
datas = {'Product' : ['A','A','A','A','A','A','A','A','A','A','A','B','B','B','B','B'],
      'SubProd' : ['BL','BL','BL','BL','BL','lk','lk','lk','lk','lk','lk','po','po','po','po','po']}
df = pd.DataFrame(data=datas, index=Index)
print(df)

Output:

           Product SubProd
4/20/2012        A      BL
4/27/2012        A      BL
5/4/2012         A      BL
5/11/2012        A      BL
5/18/2012        A      BL
4/20/2012        A      lk
4/27/2012        A      lk
5/4/2012         A      lk
5/11/2012        A      lk
5/18/2012        A      lk
5/25/2012        A      lk
10/31/2014       B      po
11/7/2014        B      po
11/14/2014       B      po
11/21/2014       B      po
11/28/2014       B      po

Thanks

Answer 1

In [10]: df['counter'] = df.groupby(['Product','SubProd']).cumcount()+1

In [11]: df
Out[11]: 
           Product SubProd  counter
4/20/2012        A      BL        1
4/27/2012        A      BL        2
5/4/2012         A      BL        3
5/11/2012        A      BL        4
5/18/2012        A      BL        5
4/20/2012        A      lk        1
4/27/2012        A      lk        2
5/4/2012         A      lk        3
5/11/2012        A      lk        4
5/18/2012        A      lk        5
5/25/2012        A      lk        6
10/31/2014       B      po        1
11/7/2014        B      po        2
11/14/2014       B      po        3
11/21/2014       B      po        4
11/28/2014       B      po        5