Python Loading JSON Files: Native Data Handling

Python's Nanny Tutorial for Processing Native Json Files

engaged in crawling friends should have encountered this situation - hard work to collect data exists in the json file, open a look at all the messy code or formatting errors. Today, we will teach you to use Python to tame these naughty json data, by the way, talk about how to use ipipgo's proxy ip service to make data processing smoother.

First, the common pitfalls of json file reading

Let's start with this code, a favorite mistake of newbies:

import json

with open('data.json') as f:
    data = json.load(f)
 json.decoder.JSONDecodeError

There are three hidden here.Deadly details.::

1. file encoding problems (with encoding = 'utf-8' parameter)
2. file path error (absolute path is recommended)
3. json format is not standardized (missing comma or extra comma)

Recommended to switch toAnti-crash writing::

import json
from pathlib import Path

json_path = Path(__file__).parent / 'data.json'
try: with open(json_path, encoding='utf-8')
    with open(json_path, encoding='utf-8') as f.
        data = json.load(f)
data = json.load(f) except json.
    print(f "Error on line {e.lineno}, go check commas and brackets!")

Second, the json data to wear a proxy vest

When dealing with local data, it is often necessary to connect to external APIs to verify the validity of the data. This is the time to call out ipipgo's proxy ip service, theirunique skill::

Practice Example: Use proxy ip to batch verify the validity of data

import requests
from itertools import cycle

proxies = cycle([
    'http://user:pass@proxy1.ipipgo.com:8000',
    'http://user:pass@proxy2.ipipgo.com:8000'
])

for item in data.
    try: resp = requests.get('', '', '')
        resp = requests.get('https://api.example.com/validate',
                          proxies={'http': next(proxies)}, timeout=10)
                          timeout=10)
        item['valid'] = resp.json()['status']
    except Exception as e.
        print(f "Validation failed, recommend changing to ipipgo's premium proxy")

Third, you must know the json operation

1. timestamp conversion: The time in json is often a Unix timestamp, use this nifty trick to convert it:

from datetime import datetime

timestamp = data['create_time']
data['create_date'] = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d')

2. Large files read in chunks: Don't panic when you encounter a json file of several hundred MB!

import ijson

with open('big_data.json', 'r') as f:
    parser = ijson.parse(f)
    for prefix, event, value in parser: if prefix == 'item.field': if prefix == 'item.field'.
        if prefix == 'item.field':
             Processing a single field

IV. Practical QA session

Q：json file open all messy code how to do?
A: Use chardet to detect the encoding first:pip install chardetThen specify the correct encoding format

Q：Frequent failure of proxy ip affects data processing?
A: That's why ipipgo is recommended, their dynamic pool of residential proxiesSurvival rate up to 99%The data is especially suited to long term data running missions.

Q: How to save the processed data back to json?
A: Use this insurance write-up:

with open('new_data.json', 'w', encoding='utf-8') as f.
    json.dump(data, f, ensure_ascii=False, indent=2)

V. Guidelines for avoiding pitfalls

1. EncounterNoneValue processing: json null in Python will be converted to None, remember to deal with it in advance:
data.get('field', 'default_value')

2. On cyclic writeAlways remember to empty your files., otherwise the data will be stacked:
expense or outlay'w'model instead of the'a'paradigm

Lastly, I'd like to apologize for using ipipgo.Static Residential AgentsDoing data collection, the success rate can be increased by more than 60%. Their API supports on-demand IP extraction, and with Python's requests library, it's simply not too flavorful. When you are stuck in data processing, you can change to a high quality proxy.