以毫秒为X轴在python中打印数据

这是毫秒分辨率的数据时间戳和当时接收到的数据字节。我想计算每秒接收的字节数

示例：在下面的数据中，在09:56:07接收到2个字节

09:56:07:457->7d
09:56:07:841->91

我希望通过将时间（以秒为单位）绘制为x轴，将字节数绘制为Y轴来可视化这些数据

Time ( Hr: Min : Sec: ms)  Data ( Packet)         
09:56:07:457  -> 7d  
09:56:07:841  -> 91  
09:56:09:008  -> 02  
09:56:09:233  -> 80  
09:56:09:345  -> 01  
09:56:10:097  -> 80  
09:56:10:448  -> 91  
09:56:11:424  -> 01  
09:56:11:552  -> 01  
09:56:12:351  -> 01  
09:56:12:607  -> 01  
09:56:12:719  -> 01  
09:56:13:711  -> 91  
09:56:14:574  -> a3  
09:56:14:766  -> 02  
09:56:15:629  -> 91  
09:56:15:773  -> 01  
09:56:16:733  -> 91  
09:56:16:877  -> 01  
09:56:17:805  -> a1  
09:58:28:921  -> 02  
09:59:00:717  -> 52  
09:59:52:459  -> 52  
10:17:44:323  -> 01  
10:17:44:467  -> 01  
10:17:45:875  -> 91  
10:17:46:018  -> 03  
10:17:46:834  -> 81  
10:17:47:010  -> 01  
10:17:47:937  -> 92  
10:17:48:738  -> 7d  
10:17:48:962  -> c9  
10:17:49:825  -> 92  
10:17:50:097  -> fb  
10:17:51:057  -> 8a  
10:17:51:952  -> 12  
10:17:52:224  -> 01  
10:17:53:936  -> 92  
10:17:54:272  -> b6  
10:22:21:282  -> 3a  
10:24:14:347  -> 01  
10:24:15:643  -> 01  
10:24:15:787  -> 01  
10:24:16:010  -> 01  
10:24:16:698  -> 01  
10:24:16:810  -> 01  
10:24:17:769  -> 91  
10:24:18:874  -> 7d  
10:24:19:689  -> 91  
10:24:19:833  -> 01  
10:24:19:945  -> 01  
10:24:20:793  -> 88  
10:24:21:752  -> 91  
10:24:21:992  -> 91  
10:24:22:760  -> 01  
10:24:23:015  -> 05  
10:24:24:072  -> 91  
10:25:24:451  -> 13  
10:25:25:442  -> 92  
10:25:27:233  -> 01  
10:25:28:384  -> 11  
10:25:29:409  -> 02  
10:26:02:484  -> 81  
10:26:02:724  -> 80  
10:26:04:132  -> 96  
10:26:05:268  -> 68  
10:26:06:084  -> 81  
10:26:07:012  -> c8  
10:26:07:331  -> a3  
10:26:08:018  -> 7d  
10:26:08:162  -> 01  
10:26:08:979  -> 91  
10:26:09:107  -> 01  
10:26:09:954  -> fe  
10:26:10:994  -> be  
10:26:11:074  -> 01  
10:26:11:186  -> 01  
10:26:12:033  -> 01  
10:26:12:273  -> 00  
10:26:13:089  -> 12  
10:26:13:201  -> 01  
10:26:14:017  -> 01  
10:26:14:161  -> 01  
10:26:15:040  -> 01  
10:26:16:048  -> 03  
10:26:16:320  -> fc  
10:26:17:119  -> 01  
10:26:17:375  -> 03  
10:26:18:175  -> b1  
10:26:18:287  -> 7d  
10:26:20:159  -> fa  
10:28:58:438  -> ea  
10:28:59:559  -> 01  
10:29:00:438  -> 3f  
10:29:01:398  -> 90  
10:29:30:491  -> fd  
10:33:15:116  -> 01  
10:43:50:509  -> 2a  
10:43:52:140  -> 06  
10:43:52:284  -> 00  
10:44:18:770  -> 00  
10:44:18:898  -> 01  
10:44:43:562  -> 01  
10:45:33:384  -> 0f  
10:45:40:134  -> 0f  
10:46:22:999  -> 01  
10:46:23:302  -> 7d  
10:46:54:445  -> 01  
10:47:34:030  -> 1f  
10:47:53:415  -> 01  
10:48:13:248  -> 2f  
10:48:47:860  -> 87  
10:49:06:334  -> d4  
10:49:24:055  -> e5  
10:49:36:867  -> b0  
10:49:37:075  -> 01  
10:49:37:186  -> 01  
10:49:57:355  -> 7d  
11:00:54:326  -> 64  
11:03:42:969  -> a5  
11:08:03:741  -> f8  
11:08:23:255  -> 2a  
11:08:40:385  -> 00  
11:08:53:676  -> 01  
11:09:14:309  -> f8  
11:11:21:320  -> fb  
11:16:21:775  -> f9  
11:16:21:887  -> dc  
11:16:22:111  -> dc  
11:16:38:041  -> dc  
11:32:46:473  -> b6  
11:34:39:089  -> 01  
11:34:39:441  -> 92  
11:34:40:593  -> 01  
11:34:40:737  -> 79  
11:34:41:617  -> 40  
11:34:41:745  -> 02  
11:34:41:872  -> 02  
11:34:42:704  -> 00  
11:34:42:832  -> 01  
11:34:42:976  -> 01  
11:34:43:664  -> 01  
11:34:43:807  -> 01  
11:34:44:031  -> 01  
11:34:44:736  -> 01  
11:34:45:088  -> 99  
11:34:45:919  -> 01  
11:34:46:159  -> 01  
11:34:46:863  -> 01  
11:34:46:991  -> 01  
11:34:47:135  -> 01  
11:34:47:966  -> 92  
11:34:48:206  -> 01  
11:34:50:078  -> 92  
11:34:51:069  -> fb  
11:34:54:203  -> cb  
11:35:04:632  -> 01  
11:35:06:008  -> d1  
11:35:07:095  -> 01  
11:35:07:223  -> 00  
11:35:07:991  -> 01  
11:35:08:311  -> 92  
11:35:09:142  -> b4  
11:35:09:366  -> 01  
11:35:10:151  -> 00  
11:35:11:222  -> 02  
11:35:11:345  -> 01  
11:35:12:213  -> 80  
11:35:13:109  -> 01  
11:35:13:237  -> 01  
11:35:14:101  -> 92  
11:35:14:228  -> 00  
11:35:29:567  -> 76  
11:35:31:071  -> c9  
11:35:32:894  -> 20  
11:35:32:990  -> 01  
11:35:33:134  -> 80  
11:35:33:822  -> 01  
11:35:34:141  -> c9  
11:35:36:030  -> fc  
11:35:36:957  -> db  
11:35:37:900  -> 7d  
11:35:38:044  -> fc  
11:35:39:052  -> a0  
11:35:40:028  -> 55  
11:35:40:220  -> 01  
11:35:41:195  -> 01  
11:35:42:187  -> 10  
11:35:43:114  -> c9  
11:36:19:135  -> 90  
11:48:45:738  -> 01  
11:48:45:962  -> 92  
11:48:47:339  -> 92  
11:48:47:465  -> 01  
11:48:48:169  -> 01  
11:48:48:521  -> 92  
11:48:49:368  -> 92  
11:48:50:329  -> 92  
11:48:50:473  -> 01  
11:48:51:384  -> c8  
11:48:51:529  -> 03  
11:48:52:312  -> 01  
11:48:53:207  -> 8b  
11:49:11:473  -> 01  
11:49:13:360  -> 02  
11:49:14:240  -> be  
11:49:14:431  -> fe  
11:49:15:200  -> 01  
11:49:19:310  -> 00  
12:02:04:725  -> 01  
12:03:15:694  -> 6f  

---

随着时间的推移，很难将这些数据可视化，因为时间太长了。以下是一种方法，可以忽略没有接收字节的时间和秒数：

d = """09:56:07:457 -> 7d
09:56:07:841 -> 91
09:56:09:008 -> 02
09:56:09:233 -> 80
09:56:09:345 -> 01
09:56:10:097 -> 80
09:56:10:448 -> 91
09:56:11:424 -> 01
09:56:11:552 -> 01
09:56:12:351 -> 01
09:56:12:607 -> 01
09:56:12:719 -> 01
09:56:13:711 -> 91
09:56:14:574 -> a3"""

import time
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

data = [i.split('->')[0].strip() for i in d.split("\n")]

#replace : with .
data = [i[:8]+"."+i[9:] for i in data]

#add date to beginning
data = ["2001-01-01 " + i for i in data]

#make new dataframe and convert strings to datetime index
df = pd.DataFrame({"ms":data})

#get milliseconds
df["ms"] = pd.to_datetime(df.ms)

#get whole seconds
df["seconds"] = [str(i)[0:19] for i in df["ms"]]
df["seconds"] = pd.to_datetime(df.seconds)
df.index = df.ms
df["bytes"] = [1 for _ in df.seconds]

print(df.head())

                                             ms             seconds  bytes
ms                                                                        
2001-01-01 09:56:07.457 2001-01-01 09:56:07.457 2001-01-01 09:56:07      1
2001-01-01 09:56:07.841 2001-01-01 09:56:07.841 2001-01-01 09:56:07      1
2001-01-01 09:56:09.008 2001-01-01 09:56:09.008 2001-01-01 09:56:09      1
2001-01-01 09:56:09.233 2001-01-01 09:56:09.233 2001-01-01 09:56:09      1
2001-01-01 09:56:09.345 2001-01-01 09:56:09.345 2001-01-01 09:56:09      1


df['string_seconds'] = [str(i) for i in df.seconds]
df = pd.DataFrame(df.string_seconds.value_counts())
df["seconds"] = pd.to_datetime(df.index)
df1 = df.sort_values('seconds')
df1.index = pd.DatetimeIndex(df1.index)

df1 = df1.iloc[1:]
df1.resample('1S')
df1.plot(x='seconds', y='string_seconds', figsize=(12,3))

---

这应该允许您查看每秒超过1字节的频率

你的问题是什么？