Python：如何在时间序列图中绘制从散点图获得的异常值？
我在做异常检测，同样，我在使用隔离林方法 我的数据：Python：如何在时间序列图中绘制从散点图获得的异常值？,python,python-3.x,matplotlib,seaborn,Python,Python 3.x,Matplotlib,Seaborn,我在做异常检测，同样，我在使用隔离林方法 我的数据： x_axis = lineplot.values[:,3] y_axis = lineplot.values[:,1] plt.figure(1) plt.plot(x_axis, y_axis) 任务的我的数据帧：lineplot是df的名称 ContextID BacksGas_Flow_sccm StepID Time_ms iso_forest 427 7290057 1.7578125 1 09:20:15.273
x_axis = lineplot.values[:,3]
y_axis = lineplot.values[:,1]
plt.figure(1)
plt.plot(x_axis, y_axis)
lineplotContextID BacksGas_Flow_sccm StepID Time_ms iso_forest
427 7290057 1.7578125 1 09:20:15.273 1
428 7290057 1.7578125 1 09:20:15.513 1
429 7290057 1.953125 2 09:20:15.744 1
430 7290057 1.85546875 2 09:20:16.814 1
431 7290057 1.7578125 2 09:20:17.833 1
432 7290057 1.7578125 2 09:20:18.852 1
433 7290057 1.7578125 2 09:20:19.872 1
434 7290057 1.7578125 2 09:20:20.892 1
435 7290057 1.7578125 2 09:20:22.42 1
436 7290057 16.9921875 5 09:20:23.82 -1
437 7290057 46.19140625 5 09:20:24.102 -1
438 7290057 46.19140625 5 09:20:25.122 -1
439 7290057 46.6796875 5 09:20:26.142 1
440 7290057 46.6796875 5 09:20:27.162 1
441 7290057 46.6796875 5 09:20:28.181 1
442 7290057 46.6796875 5 09:20:29.232 1
443 7290057 46.6796875 5 09:20:30.361 1
444 7290057 46.6796875 5 09:20:31.381 1
445 7290057 46.6796875 5 09:20:32.401 1
446 7290057 46.6796875 5 09:20:33.431 1
447 7290057 46.6796875 5 09:20:34.545 1
448 7290057 46.6796875 5 09:20:34.761 1
449 7290057 46.6796875 5 09:20:34.972 1
450 7290057 46.6796875 5 09:20:36.50 1
451 7290057 46.6796875 5 09:20:37.120 1
452 7290057 46.6796875 7 09:20:38.171 1
453 7290057 46.6796875 7 09:20:39.261 1
454 7290057 46.6796875 7 09:20:40.280 1
455 7290057 46.6796875 12 09:20:41.429 1
456 7290057 46.6796875 12 09:20:42.449 1
457 7290057 46.6796875 12 09:20:43.469 1
458 7290057 46.6796875 12 09:20:44.499 1
459 7290057 46.6796875 12 09:20:45.559 1
460 7290057 46.6796875 12 09:20:45.689 1
461 7290057 47.16796875 12 09:20:46.710 -1
462 7290057 46.6796875 12 09:20:47.749 1
463 7290057 46.6796875 15 09:20:48.868 1
464 7290057 46.6796875 15 09:20:49.889 1
465 7290057 46.6796875 16 09:20:50.910 1
466 7290057 46.6796875 16 09:20:51.938 1
467 7290057 24.21875 19 09:20:52.999 -1
468 7290057 38.76953125 19 09:20:54.27 -1
469 7290057 80.46875 19 09:20:55.68 -1
470 7290057 72.75390625 19 09:20:56.128 1
471 7290057 59.5703125 19 09:20:57.247 -1
472 7290057 63.671875 19 09:20:58.278 -1
473 7290057 70.5078125 19 09:20:59.308 -1
474 7290057 71.875 19 09:21:00.337 1
475 7290057 69.82421875 19 09:21:01.358 -1
476 7290057 69.23828125 19 09:21:02.408 -1
477 7290057 69.23828125 19 09:21:03.548 -1
478 7290057 72.4609375 19 09:21:04.597 1
479 7290057 73.4375 19 09:21:05.615 1
480 7290057 73.4375 19 09:21:06.647 1
481 7290057 73.4375 19 09:21:07.675 1
482 7290057 73.4375 19 09:21:08.697 1
483 7290057 73.4375 19 09:21:09.727 1
484 7290057 74.21875 19 09:21:10.796 1
485 7290057 75.1953125 19 09:21:11.827 1
486 7290057 75.1953125 19 09:21:12.846 1
487 7290057 75.1953125 19 09:21:13.865 1
488 7290057 75.1953125 19 09:21:14.886 1
489 7290057 75.1953125 19 09:21:15.907 1
490 7290057 75.9765625 19 09:21:16.936 1
491 7290057 75.9765625 19 09:21:17.975 1
492 7290057 75.9765625 19 09:21:18.997 1
493 7290057 75.9765625 19 09:21:20.27 1
494 7290057 75.9765625 19 09:21:21.55 1
495 7290057 75.9765625 19 09:21:22.75 1
496 7290057 75.9765625 19 09:21:23.95 1
497 7290057 76.85546875 19 09:21:24.204 1
498 7290057 76.85546875 19 09:21:25.225 1
499 7290057 76.85546875 19 09:21:25.957 1
500 7290057 76.85546875 19 09:21:26.984 1
501 7290057 75.9765625 19 09:21:27.995 1
502 7290057 75.9765625 19 09:21:29.2 1
503 7290057 76.7578125 19 09:21:30.13 1
504 7290057 76.7578125 19 09:21:31.33 1
505 7290057 76.7578125 19 09:21:32.59 1
506 7290057 76.7578125 19 09:21:33.142 1
507 7290057 76.7578125 19 09:21:34.153 1
508 7290057 75.87890625 19 09:21:34.986 1
509 7290057 75.87890625 19 09:21:35.131 1
510 7290057 75.87890625 19 09:21:35.272 1
511 7290057 75.87890625 19 09:21:35.451 1
512 7290057 76.7578125 19 09:21:36.524 1
513 7290057 76.7578125 19 09:21:37.651 1
514 7290057 76.7578125 19 09:21:38.695 1
515 7290057 76.7578125 19 09:21:39.724 1
516 7290057 76.7578125 19 09:21:40.760 1
517 7290057 76.7578125 19 09:21:41.783 1
518 7290057 76.7578125 19 09:21:42.802 1
519 7290057 76.7578125 19 09:21:43.822 1
520 7290057 76.7578125 19 09:21:44.862 1
521 7290057 76.7578125 19 09:21:45.884 1
522 7290057 76.7578125 19 09:21:46.912 1
523 7290057 76.7578125 19 09:21:47.933 1
524 7290057 76.7578125 19 09:21:48.952 1
525 7290057 76.7578125 19 09:21:49.972 1
526 7290057 76.7578125 19 09:21:51.72 1
527 7290057 77.5390625 19 09:21:52.290 1
528 7290057 77.5390625 19 09:21:52.92 1
529 7290057 77.5390625 19 09:21:53.361 1
530 7290057 77.5390625 19 09:21:54.435 1
531 7290057 76.66015625 19 09:21:55.602 1
532 7290057 76.66015625 19 09:21:56.621 1
533 7290057 72.94921875 22 09:21:57.652 1
534 7290057 3.90625 24 09:21:58.749 -1
535 7290057 2.5390625 24 09:21:59.801 -1
536 7290057 2.1484375 24 09:22:00.882 1
537 7290057 2.05078125 24 09:22:01.259 1
538 7290057 2.1484375 24 09:22:01.53 1
539 7290057 1.953125 24 09:22:02.281 1
540 7290057 1.953125 24 09:22:03.311 1
541 7290057 2.1484375 24 09:22:04.331 1
542 7290057 2.1484375 24 09:22:05.351 1
543 7290057 1.953125 24 09:22:06.432 1
544 7290057 1.85546875 24 09:22:07.519 1
545 7290057 1.7578125 24 09:22:08.549 1
546 7290057 1.85546875 24 09:22:09.710 1
547 7290057 1.7578125 24 09:22:10.738 1
548 7290057 1.85546875 24 09:22:11.798 1
549 7290057 1.953125 24 09:22:12.820 1
550 7290057 1.85546875 1 09:22:13.610 1
551 7290057 1.85546875 1 09:22:14.629 1
552 7290057 1.953125 1 09:22:15.649 1
553 7290057 1.85546875 2 09:22:16.679 1
554 7290057 1.85546875 2 09:22:17.709 1
555 7290057 1.85546875 2 09:22:18.729 1
556 7290057 1.953125 2 09:22:19.748 1
557 7290057 1.85546875 2 09:22:20.768 1
558 7290057 1.7578125 3 09:22:21.788 1
559 7290057 1.7578125 3 09:22:22.808 1
560 7290057 1.85546875 3 09:22:23.829 1
561 7290057 1.953125 3 09:22:24.848 1
562 7290057 1.85546875 3 09:22:25.898 1
563 7290057 1.953125 3 09:22:27.39 1
564 7290057 1.953125 3 09:22:28.66 1
565 7290057 1.7578125 3 09:22:29.87 1
566 7290057 1.85546875 3 09:22:30.108 1
567 7290057 1.7578125 3 09:22:31.129 1
568 7290057 1.953125 3 09:22:32.147 1
569 7290057 1.85546875 3 09:22:33.187 1
x_axis = lineplot.values[:,3]
y_axis = lineplot.values[:,1]
plt.figure(1)
plt.plot(x_axis, y_axis)
from sklearn.ensemble import IsolationForest
n_estimators = 50
iso_forest = IsolationForest(behaviour='new', n_estimators = n_estimators, max_samples = 'auto')
lineplot['iso_forest'] = iso_forest.fit_predict(lineplot.values[:,[1]])
plt.figure(2)
plt.scatter(lineplot.values[lineplot['iso_forest'] == 1, 2], lineplot.values[lineplot['iso_forest'] == 1, 1], c = 'green', label = 'Normal')
plt.scatter(lineplot.values[lineplot['iso_forest'] == -1, 2], lineplot.values[lineplot['iso_forest'] == -1, 1], c = 'red', label = 'Outlier')
谢谢您可以按如下方式操作： 您可以将两个绘图合并，然后使它们具有相同的x轴 如果您尝试以下方法：
plt.figure(2)
plt.plot(x_axis, y_axis)
plt.scatter(lineplot.values[lineplot['iso_forest'] == 1, 3], lineplot.values[lineplot['iso_forest'] == 1, 1], c = 'green', label = 'Normal')
plt.scatter(lineplot.values[lineplot['iso_forest'] == -1, 3], lineplot.values[lineplot['iso_forest'] == -1, 1], c = 'red', label = 'Outlier')
您将得到您需要的您可以按如下方式进行： 您可以将两个绘图合并，然后使它们具有相同的x轴 如果您尝试以下方法：
plt.figure(2)
plt.plot(x_axis, y_axis)
plt.scatter(lineplot.values[lineplot['iso_forest'] == 1, 3], lineplot.values[lineplot['iso_forest'] == 1, 1], c = 'green', label = 'Normal')
plt.scatter(lineplot.values[lineplot['iso_forest'] == -1, 3], lineplot.values[lineplot['iso_forest'] == -1, 1], c = 'red', label = 'Outlier')