分组/识别累积和（在 pandas 数据帧中循环）

Question

I have a data frame like below:

df = pd.DataFrame({'Letter': ['C','B','A','D','E','H','G'],
                   'Number': [5,5,5,7,7,10,10],
                   'Value of Letter': [10,15,15,25,20,30,25],
                   'Amount': [100,'','',30,'',54,''],
                   'Realisation Index': [1,3,5,2,3,4,5]
                  })

In it, I want to write a loop with the following conditions.

1. For each number in the number, column pandas should deduct the "Value of Letter" from the "Amount" column. Conditions are:
2. When deducting the value of the letter, first pandas should prioritize based on Realization index (SAY if the realization index is 1 then the relevant amount in "Value of letter" column should be deducted first and so on until all security values are deducted)
3. The highest value in "Value of Letter" should be deducted first from the amount.

I am trying to write a loop using the above conditions in python/pandas and trying to compute the "Amount 2" column.

The expected output is as follows.

df = pd.DataFrame({'Letter': ['C','B','A','D','E','H','G'],
                   'Number': [5,5,5,7,7,10,10],
                   'Value of Letter': [10,15,15,25,20,30,25],
                   'Amount': [100,'','',30,'',54,''],
                   'Realisation Index': [1,3,5,2,3,4,5],
                   'Amount 2':[90,75,60,5,-15,24,-1]
                  })

Answer 1

Let do it

df.Amount=pd.to_numeric(df.Amount,errors='coerce')
df['New']=df.sort_values('Realisation Index').groupby('Number').apply(lambda x : max(x['Amount'])-x['Value of Letter'].cumsum()).reset_index(level=0,drop=True)
df
  Letter  Number  Value of Letter  Amount  Realisation Index   New
0      C       5               10   100.0                  1  90.0
1      B       5               15     NaN                  3  75.0
2      A       5               15     NaN                  5  60.0
3      D       7               25    30.0                  2   5.0
4      E       7               20     NaN                  3 -15.0
5      H      10               30    54.0                  4  24.0
6      G      10               25     NaN                  5  -1.0