具有多个条件和列的Pandas Groupby和cumsum-Python

我有以下数据帧：

import pandas as pd

hits = {'id': ['A','A','A','A','A','A','B','B','B','C','C','C'],
        'datetime': ['2010-01-02 03:00:00','2010-01-02 03:00:14','2010-01-02 03:00:35','2010-01-02 03:00:38',
                    '2010-01-02 03:29:10','2010-01-02 03:29:35','2010-01-02 03:45:20','2010-01-02 06:10:05',
                    '2010-01-02 06:10:15','2010-01-02 07:40:15','2010-01-02 07:40:20','2010-01-02 07:40:25'],
        'uri_len': [10,20,25,15,20,10,20,25,15,30,40,45]
       }

df = pd.DataFrame(hits, columns = ['id', 'datetime','uri_len'])

df['datetime'] =  pd.to_datetime(df['datetime'], format='%Y-%m-%d %H:%M:%S')

print (df)

   id            datetime  uri_len
0   A 2010-01-02 03:00:00       10
1   A 2010-01-02 03:00:14       20
2   A 2010-01-02 03:00:35       25
3   A 2010-01-02 03:00:38       15
4   A 2010-01-02 03:29:10       20
5   A 2010-01-02 03:29:35       10
6   B 2010-01-02 03:45:20       20
7   B 2010-01-02 06:10:05       25
8   B 2010-01-02 06:10:15       15
9   C 2010-01-02 07:40:15       30
10  C 2010-01-02 07:40:20       40
11  C 2010-01-02 07:40:25       45

我想使用

id

作为分组变量，按会话对点击进行分组。对我来说，会话是一个超过15秒的非活动期（根据

datetime

列计算），或者是

uri\u len

列的减少，并且在这两种情况下都比较连续的点击

我知道如何根据每个条件单独分组：

df['session1'] = (df.groupby('id')['datetime']
               .transform(lambda x: x.diff().gt('15Sec').cumsum())
              )

df['session2'] = (df.groupby('id')['uri_len']
               .transform(lambda x: x.diff().lt(0).cumsum())
              ) 

有没有一种方法可以在同一行中组合两个转换，从而直接输出以下内容

   id            datetime  uri_len  session
0   A 2010-01-02 03:00:00       10        0
1   A 2010-01-02 03:00:14       20        0
2   A 2010-01-02 03:00:35       25        1
3   A 2010-01-02 03:00:38       15        2
4   A 2010-01-02 03:29:10       20        3
5   A 2010-01-02 03:29:35       10        4
6   B 2010-01-02 03:45:20       20        0
7   B 2010-01-02 06:10:05       25        1
8   B 2010-01-02 06:10:15       15        2
9   C 2010-01-02 07:40:15       30        0
10  C 2010-01-02 07:40:20       40        0
11  C 2010-01-02 07:40:25       45        0

---

如果我理解正确，您想添加它们吗

df['session'] = df.groupby('id')['datetime'].transform(lambda x: 
x.diff().gt('15Sec').cumsum()) + df.groupby('id')['uri_len'].transform(lambda x: 
x.diff().lt(0).cumsum())

更明确的方式是：

s1 = df.groupby('id')['datetime'].transform(lambda x: 
x.diff().gt('15Sec').cumsum())

s2 = df.groupby('id')['uri_len'].transform(lambda x: x.diff().lt(0).cumsum())

df['session'] = s1+s2