Python - 如何将 Ping 结果输出到 Pandas DataFrame 对象
Python - How to Output Ping results to Pandas DataFrame Object
这可能看起来微不足道，但我 运行 在研究如何将 ping somedomain.com 的结果存储到 Pandas Dataframe 对象时遇到了障碍。
我试过使用像 pingparsing 和 pythonping 这样的模块，但我没有任何进展。
Objective:
硬编码 ping 目标（即：ping_target = 'google.com'）
将 ping 目标的结果发送到数据框对象
使用 pandas 提取数据（即： pd.describe(ping_results) ）
我试过的
import pingparsing
import json
import numpy as np
import pandas as pd
from textwrap import dedent
parser = pingparsing.PingParsing()
ping_parser = pingparsing.PingParsing()
transmitter = pingparsing.PingTransmitter()
transmitter.destination = "google.com"
transmitter.count = 10
result = transmitter.ping()
stats = parser.parse(dedent(result))
# This "should" return each icmp_reply from the stats object.
for icmp_reply in stats.icmp_replies:
print(icmp_reply)
错误
<ipython-input-36-b7c7a41d50df> in <module>
14 transmitter.count = 10
15 result = transmitter.ping()
---> 16 stats = parser.parse(dedent(result))
17
18
C:\ProgramData\Anaconda3\lib\textwrap.py in dedent(text)
428 # all lines.
429 margin = None
--> 430 text = _whitespace_only_re.sub('', text)
431 indents = _leading_whitespace_re.findall(text)
432 for indent in indents:
TypeError: expected string or bytes-like object```
## Note
I'm not stuck on using this pingparsing module and would prefer a more organic way using python stdout but want to see if this can be possible without parsing heavily using regex. Ideally I'd want to use as much vanilla python or builtins before converting output to dataframe.
这是我对问题的解决方案：
# Import packages
import os
import pandas as pd
import numpy as np
# Define ping target, ping the target, and then store results as string
ping_target = 'google.com'
results = os.popen('ping ' + ping_target).read()
print(results)
# Extract the times from the results string
times = []
for i in range(4):
timeIdx = results.index('time')
time = results[timeIdx+5:timeIdx+7]
results = results[timeIdx+10:]
times.append(time)
# Put data into pandas DataFrame
d = {'PingNum': np.arange(1,5), 'PingTime_ms': times}
df = pd.DataFrame(data=d)
最初的“结果”变量如下所示
不需要那些os花哨的库，os 库可以胜任！
注意：此解决方案适用于 Windows，因为返回的 ping 次数始终为 4。
这可能看起来微不足道，但我 运行 在研究如何将 ping somedomain.com 的结果存储到 Pandas Dataframe 对象时遇到了障碍。
我试过使用像 pingparsing 和 pythonping 这样的模块，但我没有任何进展。
Objective:
硬编码 ping 目标（即：ping_target = 'google.com'）
将 ping 目标的结果发送到数据框对象
使用 pandas 提取数据（即： pd.describe(ping_results) ）
我试过的
import pingparsing
import json
import numpy as np
import pandas as pd
from textwrap import dedent
parser = pingparsing.PingParsing()
ping_parser = pingparsing.PingParsing()
transmitter = pingparsing.PingTransmitter()
transmitter.destination = "google.com"
transmitter.count = 10
result = transmitter.ping()
stats = parser.parse(dedent(result))
# This "should" return each icmp_reply from the stats object.
for icmp_reply in stats.icmp_replies:
print(icmp_reply)
错误
<ipython-input-36-b7c7a41d50df> in <module>
14 transmitter.count = 10
15 result = transmitter.ping()
---> 16 stats = parser.parse(dedent(result))
17
18
C:\ProgramData\Anaconda3\lib\textwrap.py in dedent(text)
428 # all lines.
429 margin = None
--> 430 text = _whitespace_only_re.sub('', text)
431 indents = _leading_whitespace_re.findall(text)
432 for indent in indents:
TypeError: expected string or bytes-like object```
## Note
I'm not stuck on using this pingparsing module and would prefer a more organic way using python stdout but want to see if this can be possible without parsing heavily using regex. Ideally I'd want to use as much vanilla python or builtins before converting output to dataframe.
这是我对问题的解决方案：
# Import packages
import os
import pandas as pd
import numpy as np
# Define ping target, ping the target, and then store results as string
ping_target = 'google.com'
results = os.popen('ping ' + ping_target).read()
print(results)
# Extract the times from the results string
times = []
for i in range(4):
timeIdx = results.index('time')
time = results[timeIdx+5:timeIdx+7]
results = results[timeIdx+10:]
times.append(time)
# Put data into pandas DataFrame
d = {'PingNum': np.arange(1,5), 'PingTime_ms': times}
df = pd.DataFrame(data=d)
最初的“结果”变量如下所示
不需要那些os花哨的库，os 库可以胜任！
注意：此解决方案适用于 Windows，因为返回的 ping 次数始终为 4。