Applying an equation by group in my python/pandas-CodePudding

I am trying to create an equation that does Revenue - Cost and them cumulates the sums by group. Is there a way to use group by with an equation? I have provided some sample data below.

Data:

stack = pd.DataFrame({'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 
'Group': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C'], 
'Revenue': [2000, 3000, 2000, 1000, 4000, 3000, 1500, 2200, 8000, 4500, 5600, 7000],
'Cost': [500, 400, 300, 700, 155, 355, 990, 1000, 800, 250, 450, 900]}, 
                  columns=['ID', 'Group', 'Revenue', 'Cost'])

stack['Profit'] = stack['Revenue'] - stack['Cost']

I tried doing this for cumsum by group but it didn't work.

stack['Total Profit'] = stack['Profit'].cumsum().groupby(['Group'])

Do I need to use lambda?

CodePudding user response：

just rearrange the statement as follows

stack['Total Profit']=stack.groupby(['Group'])['Profit'].cumsum()
stack

    ID  Group   Revenue     Cost    Profit  Total Profit
0   1       A   2000        500     1500    1500
1   2       A   3000        400     2600    4100
2   3       A   2000        300     1700    5800
3   4       A   1000        700     300     6100
4   5       B   4000        155     3845    3845
5   6       B   3000        355     2645    6490
6   7       B   1500        990     510     7000
7   8       B   2200        1000    1200    8200
8   9       C   8000        800     7200    7200
9   10      C   4500        250     4250    11450
10  11      C   5600        450     5150    16600
11  12      C   7000        900     6100    22700

CodePudding user response：

Rearranging how these methods are called will do the trick:

stack['Total Profit'] = stack.groupby('Group')['Profit'].cumsum()

now stack will have this:

    ID Group  Revenue  Cost  Profit  Total Profit
0    1     A     2000   500    1500          1500
1    2     A     3000   400    2600          4100
2    3     A     2000   300    1700          5800
3    4     A     1000   700     300          6100
4    5     B     4000   155    3845          3845
5    6     B     3000   355    2645          6490
6    7     B     1500   990     510          7000
7    8     B     2200  1000    1200          8200
8    9     C     8000   800    7200          7200
9   10     C     4500   250    4250         11450
10  11     C     5600   450    5150         16600
11  12     C     7000   900    6100         22700