Parsing JSON in Python: An Efficient Data Processing Approach

Python processing JSON encounter proxy IP when the white man first aid kit

You do data capture old iron must have encountered this kind of situation: with requests library just to the web page content into json format, suddenly the target site IP blocked. At this time, if you haveipipgoof the proxy IP pool, and can be resurrected in place in minutes to continue working.

import requests
from ipipgo import get_proxy This is our own SDK.

def safe_json_parser(url):
    proxies = {"http": get_proxy(), "https": get_proxy()}
    try.
        response = requests.get(url, proxies=proxies, timeout=10)
        return response.json()
    except Exception as e.
        print(f "Request Exception: {str(e)}")
        return None

Notice in the code theget_proxy()method, which is theipipgoIntelligent switching proxy service provided. Each request automatically change IP, than manually maintain the proxy pool is much more labor-saving , especially suitable for the need to continuously handle a large number of JSON data.

Three great uses for proxy IP in JSON processing

JSON Data Cleaning Tips

For example, let's useipipgoThe data fetched by the agent of will often have this structure:

{
  "result": [
    {
      "ip": "202.96.128.86",
      
      "carrier": "Telecom", "timestamp": "2023-08-20T14:
      "timestamp": "2023-08-20T14:22:35"
    }
    ...
  ]
}

This is the most convenient time to handle with json_normalize:

from pandas import json_normalize

data = safe_json_parser('https://api.example.com/ipdata')
df = json_normalize(data['result'])
df['timestamp'] = pd.to_datetime(df['timestamp'])

Frequently Asked Questions QA

Q: What should I do if JSON parsing fails due to proxy IP failure?
A: RecommendedipipgoThe automatic fusing mechanism will automatically switch to the backup channel when it detects 3 consecutive request failures

Q: Always report type error when dealing with nested JSON?
A: First withipipgoThe test agent ensures that the network is smooth, and then prints the complete data structure using json.dumps(data, indent=2)

Q: What if I need to process JSON from multiple data sources at the same time?
A: MatchingConcurrent proxy service for ipipgo</strong, create multiple session channels with asyncio, remember to set separate proxy parameters for each channel