代码（+一些数学）

keys = ['key1', 'key2']

cs = df1.groupby(keys)[['plus', 'minus']].cumsum()
start = df1.groupby(keys)['start'].transform('first')

df1['end'] = start + cs['plus'] - cs['minus']

结果

key1 key2  start  plus  minus  end
0    A    X      3     6      4    5
1    A    X      0     2      1    6
2    A    X      0     5      7    4
3    A    Y      8     3      1   10
4    A    Y      0     2      3    9
5    B    X      4     4      2    6
6    B    X      0     1      0    7

说明

让我们使用以下公式计算每行的值

end1 = `start1 + plus1 - minus1`
end2 = `end1 + plus2 - minus2` 
     = `start1 + (plus1 + plus2) - (minus1 + minus2)`
end3 = `end2 + plus3 - minus3`
     = `start1 + (plus1 + plus2 + plus3) - (minus1 + minus2 + minus3)`
....

如果观察公式，就会发现一个明显的模式，即 * 结束值等于组的起始值加上“加号”行的累积和减去“减号”行的累积和 *

您可以为减去plus和minus列创建helper Series，为两列创建每组的累积和，并为最后的end列添加start的第一个值，然后为start列使用DataFrameGroupBy.shift并将第一个值替换为Series.fillna中的原始值：

plusminus = df1['plus'].sub(df1['minus'])
df1 = df1.assign(plusminus = plusminus)
g = df1.groupby(['key1','key2'])

df1['end'] = g['plusminus'].cumsum().add(g['start'].transform('first'))
df1['start'] = g['end'].shift().fillna(df1['start']).astype(int)
df1 = df1.drop('plusminus', axis=1)
print (df1)
  key1 key2  start  plus  minus  end
0    A    X      3     6      4    5
1    A    X      5     2      1    6
2    A    X      6     5      7    4
3    A    Y      8     3      1   10
4    A    Y     10     2      3    9
5    B    X      4     4      2    6
6    B    X      6     1      0    7

您可以使用astype、df.iterrows()和for循环的组合执行以下操作：

import pandas as pd
import numpy as np

df = pd.DataFrame(np.array([["A", "X", 3,6,4,0], ["A", "X", 0,2,1,0], ["A", "X", 0,5,7,0], ["A", "Y", 8,3,1,0], ["A", "Y", 0,2,3,0], ["B", "X", 4,4,2,0], ["B", "X", 0,1,0,0]]),
                   columns=['key1', 'key2', 'start', 'plus', 'minus', 'end'])

# Conver columns to integer
df[['start', 'plus', 'minus', 'end']] = df[['start', 'plus', 'minus', 'end']].astype(int)

# Start the row iterator
row_iterator = df.iterrows()
# take first item from row_iterator
_, last = next(row_iterator)
# Modify the first element
last['end'] = last['start'] + last['plus'] - last['minus']
df.loc[0, :] = last
# Iterate through the rest of the rows
for i, row in row_iterator:
    # Check the keys match
    if row['key1'] == last['key1'] and row['key2'] == last['key2']:
        # Add the end of last to the start of the next row
        row['start'] = last['end']
    # Caluculate new end for row
    row['end'] = row['start'] + row['plus'] - row['minus']
    # Ensure the changes are shown in the original dataframe
    df.loc[i, :] = row
    # Last row is now the current row
    last = row

执行后，df现在是：

key1 key2  start  plus  minus  end
0    A    X      3     6      4    5
1    A    X      5     2      1    6
2    A    X      6     5      7    4
3    A    Y      8     3      1   10
4    A    Y     10     2      3    9
5    B    X      4     4      2    6
6    B    X      6     1      0    7

注意：您的df2有错误，按照您提供的逻辑，row 5的start条目应该是4而不是0。

由于apply方法是逐行工作的，因此可以如下使用，无需任何for循环：

# create your df1
df1 = pd.DataFrame(np.array([["A", "X", 3,6,4,0], ["A", "X", 0,2,1,0], ["A", "X", 0,5,7,0], ["A", "Y", 8,3,1,0], ["A", "Y", 0,2,3,0], ["B", "X", 4,4,2,0], ["B", "X", 0,1,0,0]]),
                   columns=['key1', 'key2', 'start', 'plus', 'minus', 'end'])

df1[['start','plus','minus','end']] = df1[['start','plus','minus','end']].astype(np.int32)

# a dictionary to follow-up keys and start value
d = {'AX': None,
     'AY': None,
     'BX': None}

def helper(row):
    # modify d inside this function
    global d
    # get key by concatenating key1+key2
    key = row.key1+row.key2
    # if key is already seen, use the stored value as start value
    if d[key]:
        start = d[key]
    # if key is unseen, use the df1 start value
    else:
        start=row.start
    
    # calculate end value
    end = start + row.plus - row.minus
    
    # store the end value in dictionary
    # so that it can be used as start in next corresponding row
    d[key] = end
    # update
    return start,end

# update df1 start and end row-wise
df1[['start','end']] = df1.apply(helper,axis=1,result_type='expand')

最后，更新后的df1等同于您的df2。