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python - 从 txt 文件中提取特定数字并将其插入数据框中

问题描述

晚上好，我需要一些帮助，从文本文件中提取两个数字并将它们插入到数据框中。

这是我的 txt 文件：

PING 10.0.12.100 (10.0.12.100) 56(84) bytes of data.
64 bytes from 10.0.12.100: icmp_seq=1 ttl=59 time=0.094 ms
64 bytes from 10.0.12.100: icmp_seq=2 ttl=59 time=0.070 ms
64 bytes from 10.0.12.100: icmp_seq=3 ttl=59 time=0.076 ms
64 bytes from 10.0.12.100: icmp_seq=4 ttl=59 time=0.075 ms
64 bytes from 10.0.12.100: icmp_seq=5 ttl=59 time=0.070 ms
64 bytes from 10.0.12.100: icmp_seq=6 ttl=59 time=0.060 ms
64 bytes from 10.0.12.100: icmp_seq=7 ttl=59 time=0.093 ms
64 bytes from 10.0.12.100: icmp_seq=8 ttl=59 time=0.080 ms
64 bytes from 10.0.12.100: icmp_seq=9 ttl=59 time=0.077 ms
64 bytes from 10.0.12.100: icmp_seq=10 ttl=59 time=0.082 ms
64 bytes from 10.0.12.100: icmp_seq=11 ttl=59 time=0.070 ms
64 bytes from 10.0.12.100: icmp_seq=12 ttl=59 time=0.075 ms
64 bytes from 10.0.12.100: icmp_seq=13 ttl=59 time=0.087 ms
64 bytes from 10.0.12.100: icmp_seq=14 ttl=59 time=0.069 ms
64 bytes from 10.0.12.100: icmp_seq=15 ttl=59 time=0.072 ms
64 bytes from 10.0.12.100: icmp_seq=16 ttl=59 time=0.079 ms
64 bytes from 10.0.12.100: icmp_seq=17 ttl=59 time=0.096 ms
64 bytes from 10.0.12.100: icmp_seq=18 ttl=59 time=0.071 ms

--- 10.0.12.100 ping statistics ---
18 packets transmitted, 18 received, 0% packet loss, time 17429ms
rtt min/avg/max/mdev = 0.060/0.077/0.096/0.013 ms

我想要一个这样的数据框：

在此处输入图像描述

这是我的代码：

import pandas as pd

df = pd.DataFrame(columns=["ICMP_SEQ", "TIME"])

with open("/content/H11H22_ping.txt", "r") as f:
  txt = f.read() 
  print(txt)

  // code

  df = df.append({"ICMP_SEQ": icmp_seq, "TIME": time})

谢谢

标签： pythondataframetext-mining

解决方案

使用str.extract：

df = pd.read_csv('/content/H11H22_ping.txt', skiprows=1, header=None, names=['logs'])
res = df['logs'].str.extract(r'icmp_seq=(?P<icmp_seq>\d+)\b.+\btime=(?P<time>\d+\.\d+)', expand=True)
print(res)

输出 （部分）

   icmp_seq   time
0         1  0.094
1         2  0.070
2         3  0.076
3         4  0.075
4         5  0.070
5         6  0.060
6         7  0.093
7         8  0.080
8         9  0.077
9        10  0.082
10       11  0.070
11       12  0.075
12       13  0.087
13       14  0.069
14       15  0.072
15       16  0.079
16       17  0.096
17       18  0.071
...

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