当满足另一列中的条件时，如何重置groupby cumsum？

import pandas as pd

# 创建示例DataFrame
df = pd.DataFrame({'group': ['A', 'A', 'A', 'B', 'B', 'B'],
                   'value': [1, 2, 3, 4, 5, 6]})

# 使用shift函数和条件判断重置累加值
df['cumsum_reset'] = df.groupby((df['group'] != df['group'].shift()).cumsum())['value'].cumsum()

print(df)

  group  value  cumsum_reset
0     A      1             1
1     A      2             3
2     A      3             6
3     B      4             4
4     B      5             9
5     B      6            15

import pandas as pd

# 创建示例DataFrame
df = pd.DataFrame({'group': ['A', 'A', 'A', 'B', 'B', 'B'],
                   'value': [1, 2, 3, 4, 5, 6]})

# 自定义函数实现groupby cumsum操作并重置累加值
def cumsum_reset(x):
    cumsum = x['value'].cumsum()
    reset_mask = x['group'] != x['group'].shift()
    cumsum[reset_mask] = x['value']
    return cumsum

df['cumsum_reset'] = df.groupby((df['group'] != df['group'].shift()).cumsum()).apply(cumsum_reset).reset_index(drop=True)

print(df)

  group  value  cumsum_reset
0     A      1             1
1     A      2             3
2     A      3             6
3     B      4             4
4     B      5             9
5     B      6            15