Настройка
df = pd.DataFrame({'other':range(9),
'A':[True ,False, False ,False, False ,True, False, False, False],
'B':[False,False,False,True,False,False,False,True,False]})
other A B
0 0 True False
1 1 False False
2 2 False False
3 3 False True
4 4 False False
5 5 True False
6 6 False False
7 7 False True
8 8 False False
Решение
df2 = df[df['A'].cumsum().ge(1)]
m1 = ~df2[['A','B']].any(axis = 1)
m2=(df2['A'].add(df2['B']).cumsum()%2).eq(1)
#m2=(df2['A'].add(df2['B']).cumsum()%2) #It could be enough
df_filtered = df2.loc[m1 & m2]
print(df_filtered)
other A B
1 1 False False
2 2 False False
6 6 False False