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问表的Python输出
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Stack Overflow用户

提问于 2022-02-03 11:11:52

回答 1查看 124关注 0票数 1

我正在从事一个名为“域列表”的项目，基本上是获取域名并尝试获取Org的详细信息。有一个名为lookup.icann.org的网页，我们可以获得信息，但这是一个人工过程，需要很长时间。我想通过python包实现自动化，我得到了结果，但是输出是JSON格式的。

查找有关从JSON到JSON输出创建数据from的帮助。

我的python代码如下

#pip install python-whois
#pip install pandas
import whois
import pandas as pd
import json
import ast


# test with Google domain name
data = "gmail.com"
w = whois.whois(data)

mystr = w

val = ast.literal_eval(mystr)
val1 = json.loads(json.dumps(val))
val2 = val1['domain_name'][0]['registrar'][0]
print (pd.DataFrame(val2, columns=["domain_name","registrar"]))
print(w)

产出如下

{
  "domain_name": [
    "GMAIL.COM",
    "gmail.com"
  ],
  "registrar": "MarkMonitor, Inc.",
  "whois_server": "whois.markmonitor.com",
  "referral_url": null,
  "updated_date": "2021-07-11 09:36:42",
  "creation_date": [
    "1995-08-13 04:00:00",
    "1995-08-13 07:00:00"
  ],
  "expiration_date": [
    "2022-08-12 04:00:00",
    "2022-08-11 07:00:00"
  ],
  "name_servers": [
    "NS1.GOOGLE.COM",
    "NS2.GOOGLE.COM",
    "NS3.GOOGLE.COM",
    "NS4.GOOGLE.COM",
    "ns3.google.com",
    "ns2.google.com",
    "ns1.google.com",
    "ns4.google.com"
  ],
  "status": [
    "clientDeleteProhibited https://icann.org/epp#clientDeleteProhibited",
    "clientTransferProhibited https://icann.org/epp#clientTransferProhibited",
    "clientUpdateProhibited https://icann.org/epp#clientUpdateProhibited",
    "serverDeleteProhibited https://icann.org/epp#serverDeleteProhibited",
    "serverTransferProhibited https://icann.org/epp#serverTransferProhibited",
    "serverUpdateProhibited https://icann.org/epp#serverUpdateProhibited",
    "clientUpdateProhibited (https://www.icann.org/epp#clientUpdateProhibited)",
    "clientTransferProhibited (https://www.icann.org/epp#clientTransferProhibited)",
    "clientDeleteProhibited (https://www.icann.org/epp#clientDeleteProhibited)",
    "serverUpdateProhibited (https://www.icann.org/epp#serverUpdateProhibited)",
    "serverTransferProhibited (https://www.icann.org/epp#serverTransferProhibited)",
    "serverDeleteProhibited (https://www.icann.org/epp#serverDeleteProhibited)"
  ],
  "emails": [
    "abusecomplaints@markmonitor.com",
    "whoisrequest@markmonitor.com"
  ],
  "dnssec": "unsigned",
  "name": null,
  "org": "Google LLC",
  "address": null,
  "city": null,
  "state": "CA",
  "zipcode": null,
  "country": "US"
}

所需的输出数据，因此我可以导出到excel/csv文件

domain_name|registrar|creation_date|expiration_date|name|org|address|city|zipcode|country
gmail.com|MarkMonitor, Inc|1995-08-13 07:00:00|2022-08-12 04:00:00|null|Google LLC|null|null|CA|null|USA

谢谢你的帮助

我已经修改了我的代码，现在我能够得到答案并尝试想要的答案，但是如何分割和创建一个dataframe？

import whois
import pandas as pd


data = "gmail.com"
w = whois.whois(data)

mystr = (w.domain_name,
         w.registrar,
         w.emails,
         w.name,
         w.org,
         w.address,
         w.city,
         w.zipcode,
         w.country)

print(mystr)

上述代码的输出

(['GMAIL.COM', 'gmail.com'], 'MarkMonitor, Inc.', ['abusecomplaints@markmonitor.com', 'whoisrequest@markmonitor.com'], None, 'Google LLC', None, None, None, 'US')

python

json

python-3.x

pandas

回答 1

Stack Overflow用户

回答已采纳

发布于 2022-02-05 18:43:05

你可以试试这个：

import pandas as pd
import whois


urls = ["gmail.com", "networksolutions.com", "enom.com"]  # Examples

data = {
    "domain_name": [],
    "registrar": [],
    "emails": [],
    "name": [],
    "org": [],
    "address": [],
    "city": [],
    "zipcode": [],
    "country": [],
}


for url in urls:
    w = whois.whois(url)
    for key in data.keys():
        data[key].append(w[key])

因此：

print(pd.DataFrame(data))

# Outputs
              domain_name               registrar  ...               zipcode country
0  [GMAIL.COM, gmail.com]       MarkMonitor, Inc.  ...                  None      US
1    NETWORKSOLUTIONS.COM  Network Solutions, LLC  ...            20171-6126      US
2    [ENOM.COM, enom.com]              ENOM, INC.  ...  REDACTED FOR PRIVACY      CA

票数 0

页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持

原文链接：

https://stackoverflow.com/questions/70970261

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