Question

    import pandas as pd
    import datetime
    import numpy as np
    from datetime import timedelta

    def diff_func(row):
            return (row['Timestamp'] - row['previous_end'])


    dfMockLog = [     (1, ("2017-01-01 09:00:00"), "htt://x.org/page1.html"),
                      (1, ("2017-01-01 09:01:00"), "htt://x.org/page2.html"),
                      (1, ("2017-01-01 09:02:00"), "htt://x.org/page3.html"),
                      (1, ("2017-01-01 09:05:00"), "htt://x.org/page3.html"),
                      (1, ("2017-01-01 09:30:00"), "htt://x.org/page2.html"),
                      (1, ("2017-01-01 09:33:00"), "htt://x.org/page1.html"),
                      (1, ("2017-01-01 09:37:00"), "htt://x.org/page2.html"),
                      (1, ("2017-01-01 09:41:00"), "htt://x.org/page3.html"),
                      (1, ("2017-01-01 10:00:00"), "htt://x.org/page1.html"),
                      (1, ("2017-01-01 11:00:00"), "htt://x.org/page2.html"),
                      (2, ("2017-01-01 09:41:00"), "htt://x.org/page3.html"),
                      (2, ("2017-01-01 09:42:00"), "htt://x.org/page1.html"),
                      (2, ("2017-01-01 09:43:00"), "htt://x.org/page2.html")]

    dfMockLog = pd.DataFrame(dfMockLog, columns = ['user', 'Timestamp', 'url'])
    dfMockLog['Timestamp'] = pd.to_datetime(dfMockLog['Timestamp'])
    dfMockLog = dfMockLog.sort_values(['user','Timestamp'])

    dfMockLog['previous_end'] = dfMockLog.groupby(['user'])['Timestamp'].shift(1)

    dfMockLog['time_diff'] = dfMockLog.apply(diff_func, axis=1)

    dfMockLog['cum_sum'] = dfMockLog['time_diff'].cumsum()

    print(dfMockLog)

我需要＆＃34; timediff＆＃34;要转换为秒的列。＆＃34; cum_sum＆＃34;列应包含由＆＃34; user＆＃34;分区的累计总和。如果可以分享timedelta的所有可能格式，那将是很棒的。

Answer 1

你很亲密。我喜欢的方式是通过time_diff在几秒钟内创建一个包含pd.Series.dt.seconds的新列。然后使用groupby.transform从cumsum提取user：

dfMockLog['time_diff_secs'] = dfMockLog['time_diff'].dt.seconds
dfMockLog['cum_sum'] = dfMockLog.groupby('user')['time_diff_secs'].transform('cumsum')

print(dfMockLog)

    user           Timestamp                     url        previous_end  \
0      1 2017-01-01 09:00:00  htt://x.org/page1.html                 NaT   
1      1 2017-01-01 09:01:00  htt://x.org/page2.html 2017-01-01 09:00:00   
2      1 2017-01-01 09:02:00  htt://x.org/page3.html 2017-01-01 09:01:00   
3      1 2017-01-01 09:05:00  htt://x.org/page3.html 2017-01-01 09:02:00   
4      1 2017-01-01 09:30:00  htt://x.org/page2.html 2017-01-01 09:05:00   
5      1 2017-01-01 09:33:00  htt://x.org/page1.html 2017-01-01 09:30:00   
6      1 2017-01-01 09:37:00  htt://x.org/page2.html 2017-01-01 09:33:00   
7      1 2017-01-01 09:41:00  htt://x.org/page3.html 2017-01-01 09:37:00   
8      1 2017-01-01 10:00:00  htt://x.org/page1.html 2017-01-01 09:41:00   
9      1 2017-01-01 11:00:00  htt://x.org/page2.html 2017-01-01 10:00:00   
10     2 2017-01-01 09:41:00  htt://x.org/page3.html                 NaT   
11     2 2017-01-01 09:42:00  htt://x.org/page1.html 2017-01-01 09:41:00   
12     2 2017-01-01 09:43:00  htt://x.org/page2.html 2017-01-01 09:42:00   

    time_diff  time_diff_secs  cum_sum  
0         NaT             NaN      NaN  
1    00:01:00            60.0     60.0  
2    00:01:00            60.0    120.0  
3    00:03:00           180.0    300.0  
4    00:25:00          1500.0   1800.0  
5    00:03:00           180.0   1980.0  
6    00:04:00           240.0   2220.0  
7    00:04:00           240.0   2460.0  
8    00:19:00          1140.0   3600.0  
9    01:00:00          3600.0   7200.0  
10        NaT             NaN      NaN  
11   00:01:00            60.0     60.0  
12   00:01:00            60.0    120.0

如何更改pandas中timedelta的格式

1 个答案: