如何在Pandas数据框中找到5分钟的时间间隔?
我有一个看起来像这样的熊猫数据框:
I have pandas dataframe that looks similar to this:
TIMESTAMP EVENT_COUNT
0 2014-07-23 04:28:23 1
1 2014-07-23 04:28:24 1
2 2014-07-23 04:28:25.999000 4
3 2014-07-23 04:28:27 1
4 2014-07-23 04:28:28.999000 2
5 2014-07-23 04:28:30 1
6 2014-07-23 04:29:31 7
7 2014-07-23 04:29:33 1
8 2014-07-23 04:29:34 1
9 2014-07-23 04:29:36 1
10 2014-07-23 04:40:37 2
11 2014-07-23 04:40:39 1
12 2014-07-23 04:40:40 1
13 2014-07-23 04:40:42 1
14 2014-07-23 04:40:43 1
15 2014-07-23 04:40:44.999000 4
16 2014-07-23 04:41:46 1
17 2014-07-23 04:41:47 1
18 2014-07-23 04:41:49 1
19 2014-07-23 04:41:50 1
20 2014-07-23 04:50:52 9
21 2014-07-23 04:50:53 4
22 2014-07-23 04:50:55 6
23 2014-07-27 01:12:13 1
我的最终目标是能够找到超过5分钟的差距.因此，从上面，我会发现:
My end goal is to be able to find gaps in this that exceed 5 minutes. So, from above, I'd find a gap between:
2014-07-23 04:29:36 and 2014-07-23 04:40:37
2014-07-23 04:41:50 and 2014-07-23 04:50:52
2014-07-23 04:50:55 and 2014-07-27 01:12:13
小于5分钟的间隙无需识别.因此以下内容不会被视为空白".
Gaps of less than 5 minutes do not need to be identified. So the following wouldn't be recognized as a "gap".
2014-07-23 04:28:30 and 2014-07-23 04:29:31 (Only 61 seconds)
2014-07-23 04:40:37 and 2014-07-23 04:40:39 (Only 2 seconds)
2014-07-23 04:40:44.999000 and 2014-07-23 04:41:46 (Just over 61 seconds)
如何找到上述差距?当我尝试此 answer 中提到的解决方案时，似乎没有任何改变.我使用了以下命令:
How can I find the gaps mentioned above? When I tried the solution mentioned in this answer, nothing seems to have changed. I used the following command:
df.reindex(pd.date_range(min(df['TIMESTAMP']), max(df['TIMESTAMP']), freq='5min')).fillna(0)
运行此命令后，数据帧看起来相同.
The dataframe looks the same after this command is run.
IIUC，只要dtype已经是datetime64，那么您就可以使用 dt.seconds :
IIUC so long as the dtype are datetime64 already then you can just use diff which will create a timedelta and then call the attribute dt.seconds:
In [8]:
df['OVER 5 MINS'] = (df['TIMESTAMP'].diff()).dt.seconds > 300
df
Out[8]:
TIMESTAMP EVENT_COUNT OVER 5 MINS
INDEX
0 2014-07-23 04:28:23.000 1 False
1 2014-07-23 04:28:24.000 1 False
2 2014-07-23 04:28:25.999 4 False
3 2014-07-23 04:28:27.000 1 False
4 2014-07-23 04:28:28.999 2 False
5 2014-07-23 04:28:30.000 1 False
6 2014-07-23 04:29:31.000 7 False
7 2014-07-23 04:29:33.000 1 False
8 2014-07-23 04:29:34.000 1 False
9 2014-07-23 04:29:36.000 1 False
10 2014-07-23 04:40:37.000 2 True
11 2014-07-23 04:40:39.000 1 False
12 2014-07-23 04:40:40.000 1 False
13 2014-07-23 04:40:42.000 1 False
14 2014-07-23 04:40:43.000 1 False
15 2014-07-23 04:40:44.999 4 False
16 2014-07-23 04:41:46.000 1 False
17 2014-07-23 04:41:47.000 1 False
18 2014-07-23 04:41:49.000 1 False
19 2014-07-23 04:41:50.000 1 False
20 2014-07-23 04:50:52.000 9 True
21 2014-07-23 04:50:53.000 4 False
22 2014-07-23 04:50:55.000 6 False
23 2014-07-27 01:12:13.000 1 True