Question

我有一个包含一些数据的csv文件

,date,location,device,provider,cpu,mem,load,drops,id,latency,gw_latency,upload,download,sap_drops,sap_latency,alert_id
389,2018-02-13 09:20:17.572685+00:00,ASA,10.11.100.1,BOM,4.0,23.0,0.25,0.0,,,,,,,,
390,2018-02-13 09:20:21.836284+00:00,ASA,10.11.100.1,COD,4.0,23.0,2.08,0.0,,,,,,,,
391,2018-02-13 09:30:59.401178+00:00,ASA,10.11.100.1,COD,5.0,23.0,8.0,0.0,,,,,,,,
392,2018-02-13 09:31:03.667730+00:00,ASA,10.11.100.1,COD,5.0,23.0,3.5,0.0,,,,,,,,
393,2018-02-13 09:41:14.666626+00:00,ASA,10.11.100.1,BOM,4.0,23.0,0.5,0.0,,,,,,,,
394,2018-02-13 09:41:18.935061+00:00,ASA,10.11.100.1,DAE,4.0,23.0,3.0,0.0,,,,,,,,
395,2018-02-13 09:50:17.491014+00:00,ASA,10.11.100.1,DAE,5.0,23.0,8.25,0.0,,,,,,,,
396,2018-02-13 09:50:21.751805+00:00,BBB,10.11.100.1,BOM,5.0,23.0,2.75,0.0,,,,,,,,
397,2018-02-13 10:00:18.387647+00:00,BBB,10.11.100.1,CXU,5.0,23.0,2.0,0.0,,,,,,,,
398,2018-02-13 10:00:22.847626+00:00,ASA,10.11.100.1,BOM,5.0,23.0,3.17,0.0,,,,,,,,
399,2018-02-13 10:10:17.521642+00:00,BBB,10.11.100.1,DAE,5.0,23.0,1.0,0.0,,,,,,,,
400,2018-02-13 10:10:21.786720+00:00,BBB,10.11.100.1,DAE,5.0,23.0,2.42,0.0,,,,,,,,
401,2018-02-13 10:14:38.085999+00:00,BBB,10.11.100.1,CXU,4.0,23.0,0.25,0.0,,,,,,,,
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正如您所看到的，在几个时间间隔内有很多日期2018-02-13的条目。我想将这些条目放入24小时间隔，其中每小时将包含一个值（平均值）。这就是我所做的

df_next = df.loc['2018-04-13'].resample('H')["cpu"].mean().fillna(0)

但是对于日期2018-04-13，收集的数据最多只有10:00小时（最后一个条目是10:14:38）。所以它只能让我达到这个目的。对于其他一些日期，如果数据是从9:00开始收集的，那么我只从9:00获得每小时的间隔。

如果从24开始，无论在什么时候收集数据，我如何获得从00:00开始的完整0小时间隔？所以基本上它将分配mean小时收集数据的时间没有收集数据和381,2018-02-13 00:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 382,2018-02-13 01:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 383,2018-02-13 02:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 384,2018-02-13 03:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 385,2018-02-13 04:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 386,2018-02-13 05:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 387,2018-02-13 06:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 388,2018-02-13 07:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 388,2018-02-13 08:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 389,2018-02-13 09:20:17.572685+00:00,ASA,10.11.100.1,BOM,4.0,23.0,0.25,0.0,,,,,,,, 390,2018-02-13 09:20:21.836284+00:00,ASA,10.11.100.1,COD,4.0,23.0,2.08,0.0,,,,,,,, 391,2018-02-13 09:30:59.401178+00:00,ASA,10.11.100.1,COD,5.0,23.0,8.0,0.0,,,,,,,, 392,2018-02-13 09:31:03.667730+00:00,ASA,10.11.100.1,COD,5.0,23.0,3.5,0.0,,,,,,,, 393,2018-02-13 09:41:14.666626+00:00,ASA,10.11.100.1,BOM,4.0,23.0,0.5,0.0,,,,,,,, 394,2018-02-13 09:41:18.935061+00:00,ASA,10.11.100.1,DAE,4.0,23.0,3.0,0.0,,,,,,,, 395,2018-02-13 09:50:17.491014+00:00,ASA,10.11.100.1,DAE,5.0,23.0,8.25,0.0,,,,,,,, 396,2018-02-13 09:50:21.751805+00:00,BBB,10.11.100.1,BOM,5.0,23.0,2.75,0.0,,,,,,,, 397,2018-02-13 10:00:18.387647+00:00,BBB,10.11.100.1,CXU,5.0,23.0,2.0,0.0,,,,,,,, 398,2018-02-13 10:00:22.847626+00:00,ASA,10.11.100.1,BOM,5.0,23.0,3.17,0.0,,,,,,,, 399,2018-02-13 10:10:17.521642+00:00,BBB,10.11.100.1,DAE,5.0,23.0,1.0,0.0,,,,,,,, 400,2018-02-13 10:10:21.786720+00:00,BBB,10.11.100.1,DAE,5.0,23.0,2.42,0.0,,,,,,,, 401,2018-02-13 10:14:38.085999+00:00,BBB,10.11.100.1,CXU,4.0,23.0,0.25,0.0,,,,,,,, 402,2018-02-13 11:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 403,2018-02-13 12:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 404,2018-02-13 13:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 405,2018-02-13 14:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 406,2018-02-13 15:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 407,2018-02-13 16:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 408,2018-02-13 17:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 409,2018-02-13 18:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 410,2018-02-13 19:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 411,2018-02-13 20:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 412,2018-02-13 21:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 413,2018-02-13 22:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 414,2018-02-13 23:00:00.000000+00:00,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0？

所以基本上我想要这样的东西

正如您所看到的那样，它会填满其余时间，并将其值保持为html2canvas.js:373 Invalid value given for Length: "auto"。

Answer 1

使用：

runfile('/path/to/script.py')

如何将时间序列数据捆绑到熊猫的24小时间隔内？

1 个答案: