具有多個條件和列的 Pandas Groupby 和 cumsum - Python

Question

我有以下數據框：

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

我想按會話對點擊進行分組，使用id作為按變量分組。 對我來說，一個會話是一個超過 15 秒的不活動期（從datetime列計算），或者uri_len列的減少，並且在這兩種情況下比較連續的命中。

我知道如何分別按每個條件分組：

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())
              ) 

有沒有辦法在同一行中組合兩個轉換，所以輸出直接是這樣的？：

   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

Answer 1

如果我理解正確，您要添加它們嗎？

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())

更清晰的方法：

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