JSON to CSV: Python/Pandas One-Click Data Format Conversion

Hands-on with Python to convert JSON data to CSV table

Brothers engaged in data processing understand that JSON and CSV are like spicy hot and clear soup pot - each has its own way of eating. Today we do not talk about false, directly on the hard food: how to use Python's Pandas library to JSON files into CSV with one key. do not rush, here are a few pitfalls have to give you a wake-up call.

Why do you need a proxy IP for data conversion?

For example, when you pick up data from different websites (let's say e-commerce price comparison, opinion monitoring), it is easy to trigger the anti-climbing mechanism by brushing with your own IP. At this timeDynamic Residential Proxy for ipipgoIt'll come in handy, it'll help you:

Four steps to format conversion

Step 1: Load up your gear
Hit this in the terminal (remember to activate the virtual environment first):

pip install pandas requests

Step 2: Read the JSON file
Let's say we have a json file of order data:

import pandas as pd
data = pd.read_json('orders.json', encoding='gbk') Chinese garbage killer

Step 3: Dealing with nested structures
It's a tough nut to crack:

{
  "user": "Laozhang",
  "items": [
    {"name": "Keyboard", "price":299}, {"name": "Mouse", "price":199}, {"user": "Lao Zhang", "items": [
    {"name": "mouse", "price":199}
  ]
}

Expand nesting with this tart operation:

from pandas.io.json import json_normalize
df = json_normalize(data, 'items', ['user'])

Step 4: Save as CSV
A perfect finish:

df.to_csv('output.csv', index=False, encoding='utf_8_sig')

Practical Tips and Tricks

1. Beware of handling large files: For json files over 100M, it is recommended to use theipipgo's exclusive bandwidth proxySegmented downloads. Don't blow your own network card.
2. Date format to be harmonized: add convert_dates=['create_time'] parameter to read_json
3. Exception handling can't be understated: Wrap key steps in try...except to avoid scripts crashing in the middle of the process

Frequently Asked Questions QA

Q: What should I do if the Chinese is garbled after conversion?
A: add encoding='utf_8_sig' parameter to to_csv, it works.

Q: What about multiple nested hierarchies in json?
A: Use the meta parameter of json_normalize to pick apart the layers, such as meta=['user',['location','city ']]

Q: What if I need to convert automatically at regular intervals?
A: MatchingAPI proxy for ipipgoWrite a timed task, remember to set the retry mechanism and proxy IP auto change

Why do you recommend ipipgo?

Recently helped a friend's company to do data migration, every day to deal with 50G + json logs. The actual test found:
- It takes 26 minutes to convert 100,000 pieces of data with a normal proxy.
- changeipipgo's s5 proxy programAfter that, the same amount of data in as little as 8 minutes
The key is their homeLong-lasting static residential IPIt can maintain a stable connection when doing data synchronization, and won't drop out halfway through the conversion.

Next time you have a json to csv requirement, don't freeze. Load Pandas first, and then the entireProxy services for ipipgo, ensuring that your data processing efficiency takes off straight away. See you in the comments section if you don't understand anything!