python之有条件的 groupby CumCount pandas

我有这个数据框:

    dic = {'users' : ['A','A','B','A','A','B','A','A','A','A','A','B','A'], 
            'product' : [1,1,2,2,1,2,1,2,1,1,2,1,1], 
            'action' : ['see', 'see', 'see', 'see', 'buy', 'buy', 'see', 'see', 'see', 'see', 'buy', 'buy', 'buy'] 
    } 
 
df = pd.DataFrame(dic, columns=dic.keys()) 
 
df 
 
 
users   product action 
0   A   1   see 
1   A   1   see 
2   B   2   see 
3   A   2   see 
4   A   1   buy 
5   B   2   buy 
6   A   1   see 
7   A   2   see 
8   A   1   see 
9   A   1   see 
10  A   2   buy 
11  B   1   buy 
12  A   1   buy 

我需要的是一个列，用于计算每个用户在购买产品之前看到产品的次数。

结果应该是这样的:

dic = {'users' : ['A','A','B','A','A','B','A','A','A','A','A','B','A'], 
        'product' : [1,1,2,2,1,2,1,2,1,1,2,1,1], 
        'action' : ['see', 'see', 'see', 'see', 'buy', 'buy', 'see', 'see', 'see', 'see', 'buy', 'buy', 'buy'], 
        'see_before_buy' : [1,2,1,1,2,1,1,2,2,3,2,0,3] 
} 
 
users   product action  see_before_buy 
0   A   1   see 1 
1   A   1   see 2 
2   B   2   see 1 
3   A   2   see 1 
4   A   1   buy 2 
5   B   2   buy 1 
6   A   1   see 1 
7   A   2   see 2 
8   A   1   see 2 
9   A   1   see 3 
10  A   2   buy 2 
11  B   1   buy 0 
12  A   1   buy 3 

有人能帮帮我吗？

请您参考如下方法:

您可能需要在 shift

addkey=df.groupby(['user','#product']).action.apply(lambda x : x.eq('buy').shift().fillna(0).cumsum()) 
df['seebefore']=df.action.eq('see').groupby([df.user,df['#product'],addkey]).cumsum() 
df 
Out[131]:  
    index user  #product action  seebefore 
0       0    A         1    see        1.0 
1       1    A         1    see        2.0 
2       2    B         2    see        1.0 
3       3    A         2    see        1.0 
4       4    A         1    buy        2.0 
5       5    B         2    buy        1.0 
6       6    A         1    see        1.0 
7       7    A         2    see        2.0 
8       8    A         1    see        2.0 
9       9    A         1    see        3.0 
10     10    A         2    buy        2.0 
11     11    B         1    buy        0.0 
12     12    A         1    buy        3.0