python - 来自迭代多个 URL 的 JSON 响应以存储 DataFrame

Question

我有动态 API URL，每个 URL 都以 JSON 形式获取响应数据，如下所示。

{
   "@type":"connection",
   "id":"001ZOZ0B00000000006Z",
   "orgId":"001ZOZ",
   "name":"WWW3",
   "description":"Test connection2",
   "createTime":"2018-07-20T18:28:05.000Z",
   "updateTime":"2018-07-20T18:28:53.000Z",
   "createdBy":"xx.xx@xx.com.dev",
   "updatedBy":"xx.xx@xx.com.dev",
   "agentId":"001ZOZ08000000000007",
   "runtimeEnvironmentId":"001ZOZ25000000000007",
   "instanceName":"ShareConsumer",
   "shortDescription":"Test connection2",
   "type":"TOOLKIT",
   "port":0,
   "majorUpdateTime":"2018-07-20T18:28:05.000Z",
   "timeout":60,
   "connParams":{
      "WSDL URL":"https://xxxservices1.work.com/xxx/service/xxport2/n5/Integration%20System/API__Data?wsdl",
      "Must Understand":"true",
      "DOMAIN":"n5",
      "agentId":"001ZOZ0800XXX0007",
      "agentGroupId":"001ZOZ25000XXX0007",
      "AUTHENTICATION_TYPE":"Auto",
      "HTTP Password":"********",
      "Encrypt password":"false",
      "orgId":"001Z9Z",
      "PRIVATE_KEY_FILE":"",
      "KEY_FILE_TYPE":"PEM",
      "mode":"UPDATE",
      "CERTIFICATE_FILE_PASSWORD":null,
      "CERTIFICATE_FILE":null,
      "TRUST_CERTIFICATES_FILE":null,
      "Username":"xxx@xxx",
      "CERTIFICATE_FILE_TYPE":"PEM",
      "KEY_PASSWORD":null,
      "TIMEOUT":"60",
      "Endpoint URL":"https://wxxservices1.xx.com/xxx/service/xxport2/n5/Integration%20System/API__Data",
      "connectionTypes":"NOAUTH",
      "HTTP Username":"API@n5",
      "Password":"********"
   }
}

现在要注意的是，我有大约 50 个提供这种类型 JSON 数据的 URL。我正在使用以下代码对其进行迭代，但我无法将每个 URL 的每个响应存储在 Python pandas 数据框中。 它将是仅存储在那里的最后一个响应。

我还想将整个数据框转换为 CSV。

将 URL 响应的每个结果的响应附加到数据帧然后转换为 CSV 的最佳方法是什么？

Python代码如下:

import requests
from urllib.request import Request, urlopen
from urllib.request import urlopen, URLError, HTTPError
import urllib.error
import json
import pandas as pd
from pandas.io.json import json_normalize
import os
import csv

#This CSV file where we are getting ID and iterating over it for each url for get JSON data for the each URL
ConnID_data_read=pd.read_csv('ConnID.csv', delimiter = ',')
df = pd.DataFrame(ConnID_data_read)



user_iics_loginURL='https://xx-us.xxx.com/ma/api/v2/user/login'

headers = {
        'Content-Type': "application/json",
        'Accept': "application/json",
        'cache-control': "no-cache"

        }


payload = "{\r\n\"@type\": \"login\",\r\n\"username\": \"xx@xx.com.xx\",\r\n\"password\": \"xxxx\"\r\n}"

response = requests.request("POST", user_iics_loginURL, data=payload, headers=headers)
resp_obj = json.loads(response.text)
session_id = resp_obj['SessionId']
server_URL = resp_obj['serverUrl']
print(session_id)
Finaldf = pd.DataFrame()
for index, row in df.iterrows():

    api_ver="/api/v2/connection/"+row['id']
    #https://xx-us.xxx.com/saas/api/v2/connection/001ZOZ0B000000000066
    conndetails_url = server_URL+api_ver
    print(conndetails_url)
    act_headers = {
    'icSessionId': session_id,
    'Content-Type': "application/json",
    'cache-control': "no-cache",

    }
    act_response = requests.get(conndetails_url.strip(),headers=act_headers)
    print(act_response.text)
    print("Creating Data Frame on this***********************")
    act_json_data= json.loads(act_response.text)
    flat_json = json_normalize(act_json_data)
    print(flat_json)
    Conndf = pd.DataFrame(flat_json)

    Finaldf.append(Conndf)
Finaldf.to_csv('NewTest.csv')

Answer 1

我首先注意到的是:

flat_json = json_normalize(act_json_data)
print(flat_json)
Conndf = pd.DataFrame(flat_json)

当你执行 flat_json = json_normalize(act_json_data) 时，flat_json 已经是一个数据帧。执行 Conndf = pd.DataFrame(flat_json) 是不必要且多余的，尽管不会造成问题，但它只是您不需要的额外代码。

其次是问题。附加数据帧时，需要将其设置为等于自身。所以改变:

Finaldf.append(Conndf)

到

Finaldf = Finaldf.append(Conndf)

我也只是休息索引，因为这只是我追加数据帧时的习惯:

Finaldf = Finaldf.append(Conndf).reset_index(drop=True)

除了那 ​​1 行，它看起来不错，您应该使用 Finaldf.to_csv('NewTest.csv') 将完整的数据帧保存到 csv 中