Seeking Advice on Efficient Pandas Operations for Conditional Summing

cols = ['employeeID', 'groupName', 'vestingyear', 'agreementDate']
out = (
   df.merge(
       df.groupby(cols)['vestingvalueCAD'].sum()
         .groupby(cols[:-1]).cumsum()
         .rename('totalvestingvalueCAD'), 
       on=cols
   )
   .assign(
       totalvestingvalueCAD=lambda x: x['totalvestingvalueCAD'] 
       - x['vestingvalueCAD']
       )
   )

   employeeID groupName  agreementID agreementDate  trancheID  vestingyear  \
0           2         A            7    2025-03-01         28          2025   
1           2         A            7    2025-03-01         29          2026   
2           2         A            1    2025-04-01         26          2026   
3           2         A            1    2025-03-01         27          2027   
4           2         A            8    2025-02-01         30          2026   
5           2         B            9    2025-03-01         31          2026   
6           2         A            6    2025-03-01         32          2026   
   vestingvalueCAD  totalvestingvalueCAD  
0                200                        0  
1                300                       90  
2                400                      390  
3                500                        0  
4                 50                        0  
5                 30                        0  
6                 40                      350  

• Start with .groupby + groupby.sum to get the sum per date:

cols = ['employeeID', 'groupName', 'vestingyear', 'agreementDate']
employeeID  groupName  vestingyear  agreementDate
2           A          2025          2025-03-01       200
                       2026          2025-02-01        50
                                     2025-03-01       340
                                     2025-04-01       400
                       2027          2025-03-01       500
            B          2026          2025-03-01        30
Name: vestingvalueCAD, dtype: int64

• Now, chain a groupby on the same columns (now: index levels) except 'agreementDate' (cols[:-1]) + groupby.cumsum + Series.rename:

(df.groupby(cols)['vestingvalueCAD'].sum()
   .groupby(cols[:-1]).cumsum()
   .rename('totalvestingvalueCAD'))
employeeID  groupName  vestingyear  agreementDate
2           A          2025          2025-03-01       200
                       2026          2025-02-01        50
                                     2025-03-01       390
                                     2025-04-01       790
                       2027          2025-03-01       500
            B          2026          2025-03-01        30
Name: totalvestingvalueCAD, dtype: int64

• Use .merge on cols to add to the original df and use .assign to correct column 'totalvestingvalueCAD' by subtracting column 'vestingvalueCAD'.

df.merge(...).loc[:, ['vestingvalueCAD', 'totalvestingvalueCAD']]
   vestingvalueCAD  totalvestingvalueCAD
0                200                      200 # 200 - 200 = 0
1                300                      390 # 390 - 300 = 90
2                400                      790 # 790 - 400 = 390
3                500                      500 # etc.
4                 50                       50
5                 30                       30
6                 40                      390