Python逻辑操作作为Pandas中的条件

您可以使用用途:

# identify start of group
m1 = df.eq([False, False]).all(axis=1)
# condition
m2 = df.eq([True, False]).all(axis=1)
# form groups
group = m1.cumsum()

# keep only rows with valid condition and after a start of group
# get the first value per group
idx = m2[m2 & (group>0)].groupby(group).idxmax().tolist()

# variant
# idx = m2.index.to_series()[m2 & (group>0)].groupby(group).first().tolist()

# assign flag
df.loc[idx, 'flag'] = 'X'

输出:

       A      B flag
0  False   True  NaN
1   True  False  NaN
2  False  False  NaN
3  False  False  NaN
4  False   True  NaN
5  False   True  NaN
6   True  False    X
7   True  False  NaN
8  False  False  NaN
9   True  False    X

中间体:

       A      B     m1     m2  group flag
0  False   True  False  False      0     
1   True  False  False   True      0     
2  False  False   True  False      1     
3  False  False   True  False      2     
4  False   True  False  False      2     
5  False   True  False  False      2     
6   True  False  False   True      2    X
7   True  False  False   True      2     
8  False  False   True  False      3     
9   True  False  False   True      3    X