如何转换日期的时间并计算python中的天数？

Question

我有一个关于用户参加在线课程的数据集。它具有“id”、“事件”、“时间”等功能。我对他们进行分组，并想知道用户在特定日期执行每个事件的频率。我想以天数来计算。

lt = log_train.groupby(['enrollment_id','event','time']).size()
print(lt)


enrollment_id  event       time
1              access  2014-06-14T09:38:39    2
                       2014-06-14T09:38:48    1
                       2014-06-19T06:21:16    2
                       2014-06-19T06:21:32    1
                       2014-06-19T06:21:45    1
                                           ..
200887     navigate    2014-07-24T03:27:16    1
200887     navigate    2014-07-24T03:27:16    1
           page_close  2014-07-24T04:19:55    1
           video       2014-07-24T04:19:57    1
200888     access      2014-07-24T03:48:14    2
           discussion  2014-07-24T03:47:57    1
           navigate    2014-07-24T03:47:17    1
                       2014-07-24T03:47:28    1
                       2014-07-24T03:48:01    1

根据我在另一个数据集中看到的信息，有用户 ID、课程 ID 和课程范围时间。

usercourse = pd.merge(enroll,date,how="left", on= 'course_id' )



 enrollment_id                          username  \

0                   1  9Uee7oEuuMmgPx2IzPfFkWgkHZyPbWr0   
1                   3  1qXC7Fjbwp66GPQc6pHLfEuO8WKozxG4   
2                   4  FIHlppZyoq8muPbdVxS44gfvceX9zvU7 

                           course_id        from          to  
0       DPnLzkJJqOOPRJfBxIHbQEERiYHu5ila  2014-06-12  2014-07-11  
1       7GRhBDsirIGkRZBtSMEzNTyDr2JQm4xx  2014-06-19  2014-07-18  
2       DPnLzkJJqOOPRJfBxIHbQEERiYHu5ila  2014-06-12  2014-07-11 

每个用户只有 1 门课程，所有课程的范围相同，均为 30 天。所以我想要的应该是这样的，

enrollment_id  event      #ofDays   #ofActionTimes
1              access      2         2
                           10        6
                           30        2
                                   ..
200887         navigate    23        1
               page_close  30        1
               video       1         1
200888         access      12        2
               discussion  2         1
               navigate    5         3
                           29        4  

**#ofDays means at the Nth day of a course.
#ofActionTimes means how often an event happens on the Nth day.**

由于每门课程都是从不同的日期开始的，我不知道如何在 python 上生成这个数据表单。
希望有人能帮我解决问题！

Answer 1

IIUC，您可以使用merge、groupby 和count 来获得您想要的。

首先，一些示例数据。这是基于您提供的数据，但我已对其进行了修改，以便可以从起始数据清楚地跟踪输出。

data1 = {"enrollment_id":[1,1,1,1,2,2,3,3,3],
         "event":["access","access","access","navigate","access",
                  "page_close","navigate","navigate","video"],
         "time":["2014-06-14T09:38:39", "2014-06-14T09:38:48",
                 "2014-06-19T06:21:16", "2014-06-19T06:21:32", 
                 "2014-06-21T06:21:45", "2014-06-22T06:21:16",
                 "2014-06-19T06:21:32", "2014-06-20T06:21:16",
                 "2014-06-20T06:21:16"]}

data2 = {"enrollment_id":[1,2,3],
         "username":["user1", "user2", "user3"],
         "course_id":["course1", "course2", "course3"],
         "course_from":["2014-06-12", "2014-06-19", "2014-06-12"],
         "course_to":["2014-07-11", "2014-07-18", "2014-07-11"]}

df1 = pd.DataFrame(data1)
df1
   enrollment_id       event                 time
0              1      access  2014-06-14T09:38:39
1              1      access  2014-06-14T09:38:48
2              1      access  2014-06-19T06:21:16
3              1    navigate  2014-06-19T06:21:32
4              2      access  2014-06-21T06:21:45
5              2  page_close  2014-06-22T06:21:16
6              3    navigate  2014-06-19T06:21:32
7              3    navigate  2014-06-20T06:21:16
8              3       video  2014-06-20T06:21:16

df2 = pd.DataFrame(data2)
df2
  course_id  enrollment_id course_from   course_to username
0   course1              1  2014-06-12  2014-07-11    user1
1   course2              2  2014-06-19  2014-07-18    user2
2   course3              3  2014-06-12  2014-07-11    user3

我们想知道特定event 发生了多少次针对特定enrollment_id，课程的每一天都有单独的计数。

通过从event_date 中减去course_from（课程开始日期）得出课程天数course_day_num。

df = (df1.merge(df2[["enrollment_id", "course_from"]], 
           on="enrollment_id", how="left")
)
df["event_date"] = pd.to_datetime(pd.to_datetime(df1.time).dt.date)
df["course_from"] = pd.to_datetime(df["course_from"])
df["course_day_num"] = (df.event_date - df["course_from"]).dt.days

然后groupby 每个course_day_num 得到事件计数，每人，每课程天：

groupby_cols = ["enrollment_id", "event", "event_date", "course_day_num"]

df.groupby(groupby_cols).event_date.count()

enrollment_id  event       event_date  course_day_num
1              access      2014-06-14  2                 2
                           2014-06-19  7                 1
               navigate    2014-06-19  7                 1
2              access      2014-06-21  2                 1
               page_close  2014-06-22  3                 1
3              navigate    2014-06-19  7                 1
                           2014-06-20  8                 1
               video       2014-06-20  8                 1
Name: event_date, dtype: int64