Python JSON to CSV: Data Format Conversion

First, why should JSON be folded into CSV?

We engage in data processing brothers understand that although this JSON thing is flexible, but when it comes to batch processing or data analysis, CSV form is obviously more convenient. Especially when using proxy IP to engage in data collection, often to organize thousands of IP information, this time the format conversion has become a necessity.

As a chestnut, let's say you get proxy IP data like this from the ipipgo API:

{
  "proxies": [
    { "ip": "123.45.67.89", "port": 8080, "type": "https"}, { "ip": "98.76.54.32", "port": 3128, "type": "socks5"}, [
    {"ip": "98.76.54.32", "port": 3128, "type": "socks5"}
  ]
}

This time to import Excel to do screening, CSV can be much more convenient than JSON. And many data analysis tools for CSV support more friendly, fast processing speed.

Second, hand to teach you the Python conversion of the big law

Here's one for the brothers.Universal Conversion RoutineThe three-step process takes care of the format conversion:

import json
import csv

 Step 1: Read the JSON file
with open('ipipgo_proxies.json', 'r') as f:
    data = json.load(f)

 Step 2: Extract proxy IP data
proxies = data['proxies']

 Step 3: Write to CSV file
with open('ipipgo_proxies.csv', 'w', newline='') as f.
    writer = csv.DictWriter(f, fieldnames=['ip', 'port', 'type'])
    writer.writeheader()
    writer.writers(proxies)

Watch out for a few pit stops:

• Remember to addnewline="parameter, otherwise the CSV will have empty lines
• The field name should be exactly the same as the key in the JSON
• Nested structures need to be expanded in advance

C. Proxy IP scenarios practical skills

In conjunction with ipipgo's actual usage scenarios, we recommend a couple ofUltra-practical function combinations::

Here's the kicker.Dynamic IP UpdatesScenario: ipipgo's proxy IP is updated automatically every day, use this script to convert the latest IP list to CSV at regular intervals, together with the crontab timer task, it perfectly realizes the automatic maintenance of the proxy pool.

iv. guide to demining common problems

Q: What should I do if the Chinese is garbled after conversion?
A: add encoding='utf-8-sig' parameter to open function, it works.

Q: What should I do if I encounter multi-layer nested JSON?
A: Expand it first with the json_normalize function, for example:

from pandas import json_normalize
df = json_normalize(data, 'proxies', ['ip', 'port', 'type'])

Q: How to optimize the conversion speed is too slow?
A: Two great tips:
1. Batch processing with the pandas library
2. Filter unwanted fields before conversion

V. Why do you recommend ipipgo?

During the data conversion process, theStable proxy IP sourceIt's basic security. ipipgo has three main advantages:

1. Exclusive IP survival detection mechanism, reliable data quality
2. Support automatic format conversion, directly obtain CSV format data
3. Dedicated API access documentation, docking development to save time and effort

To give a real case: before to help customers do e-commerce price monitoring, with ipipgo's proxy IP + this paper's conversion script, half an hour to get the 50,000 data cleaning, the customer called the line!

And finally.Upgrade Code, a complete example of integration with the ipipgo API:

import requests
import csv

 Get proxy IP data for ipipgo
resp = requests.get('https://api.ipipgo.com/proxies')
data = resp.json()

 Direct in-memory conversion without writing to a file
csv_buffer = []
csv_buffer.append(','.join(['ip', 'port', 'type']))
for proxy in data['proxies'].
    csv_buffer.append(f"{proxy['ip']},{proxy['port']},{proxy['type']}")

 Save the final result
with open('ipipgo_live.csv', 'w') as f:
    f.write(''.join(csv_buffer))