根据连续时间步长计算事件

Question

我在pandas数据帧中有1分钟时间步数据.此数据不会连续记录,现在我想根据以下条件将所有数据拆分为单独的事件:如果连续数据记录5分钟或更长时间,则仅将其视为事件,并且对于此类事件数据需要单独提取.有没有办法在pandas数据帧中实现它.

我的数据看起来像这样(结果是Event列):

Date                  X    Event
2017-06-06 01:08:00 0.019   1
2017-06-06 01:09:00 0.005   1
2017-06-06 01:10:00 0.03    1
2017-06-06 01:11:00 0.005   1
2017-06-06 01:12:00 0.003   1
2017-06-06 01:13:00 0.001   1
2017-06-06 01:14:00 0.039   1
2017-06-06 01:15:00 0.003   1
2017-06-06 01:17:00 0.001   nan
2017-06-06 01:25:00 0.006   nan
2017-06-06 01:26:00 0.006   nan
2017-06-06 01:27:00 0.032   nan
2017-06-06 01:29:00 0.013   2
2017-06-06 01:30:00 0.065   2
2017-06-06 01:31:00 0.013   2
2017-06-06 01:32:00 0.001   2
2017-06-06 01:33:00 0.02    2
2017-06-06 01:38:00 0.05    nan
2017-06-06 01:40:00 0.025   3
2017-06-06 01:41:00 0.01    3
2017-06-06 01:42:00 0.008   3
2017-06-06 01:43:00 0.009   3
2017-06-06 01:44:00 0.038   3
2017-06-06 01:45:00 0.038   3

您的建议非常感谢.

使用nnnmmm提供的解决方案,结果如下所示

2015-01-01 03:24:00       NaN
2015-01-01 04:59:00       NaN
2015-01-01 05:01:00       NaN
2015-01-01 05:02:00       NaN
2015-01-01 05:03:00       NaN
2015-01-13 01:12:00       1.0
2015-01-13 01:13:00       1.0
2015-01-13 01:14:00       1.0
2015-01-13 01:15:00       1.0
2015-01-13 01:16:00       1.0
2015-01-13 01:49:00       1.0
2015-01-13 01:50:00       1.0
2015-01-13 01:51:00       1.0
2015-01-13 01:52:00       1.0
2015-01-13 01:53:00       1.0
2015-01-13 01:54:00       1.0
2015-01-13 01:55:00       1.0

在这种情况下,在01:16:00和01:49:00之间有时间变化,它不应该将其视为同一事件,而01:49:00应该是第二个事件.

Answer 1

这有点粗糙(并不是非常简洁),但你可以做类似下面的事情(你当然可以使它更简洁并省略中间变量,但我把它们放在这里以便更容易理解发生了什么).

df['new'] = (df.reset_index().Date.diff() == pd.Timedelta('1min')).astype(int).values
df['grp'] = (df.new != 1).cumsum()
df['cnt'] = df.groupby('grp')['new'].transform(size)

df['event'] = df['cnt'] > 4
df['Event'] = ((df.event) & (df.new != 1)).cumsum()
df['Event'] = np.where( df.event, df.Event, np.nan )

                         X  new  grp  cnt  event  Event
Date                                                   
2017-06-06 01:08:00  0.019    0    1    8   True    1.0
2017-06-06 01:09:00  0.005    1    1    8   True    1.0
2017-06-06 01:10:00  0.030    1    1    8   True    1.0
2017-06-06 01:11:00  0.005    1    1    8   True    1.0
2017-06-06 01:12:00  0.003    1    1    8   True    1.0
2017-06-06 01:13:00  0.001    1    1    8   True    1.0
2017-06-06 01:14:00  0.039    1    1    8   True    1.0
2017-06-06 01:15:00  0.003    1    1    8   True    1.0
2017-06-06 01:17:00  0.001    0    2    1  False    NaN
2017-06-06 01:25:00  0.006    0    3    3  False    NaN
2017-06-06 01:26:00  0.006    1    3    3  False    NaN
2017-06-06 01:27:00  0.032    1    3    3  False    NaN
2017-06-06 01:29:00  0.013    0    4    5   True    2.0
2017-06-06 01:30:00  0.065    1    4    5   True    2.0
2017-06-06 01:31:00  0.013    1    4    5   True    2.0
2017-06-06 01:32:00  0.001    1    4    5   True    2.0
2017-06-06 01:33:00  0.020    1    4    5   True    2.0
2017-06-06 01:38:00  0.050    0    5    1  False    NaN
2017-06-06 01:40:00  0.025    0    6    6   True    3.0
2017-06-06 01:41:00  0.010    1    6    6   True    3.0
2017-06-06 01:42:00  0.008    1    6    6   True    3.0
2017-06-06 01:43:00  0.009    1    6    6   True    3.0
2017-06-06 01:44:00  0.038    1    6    6   True    3.0
2017-06-06 01:45:00  0.038    1    6    6   True    3.0