Efficient JSON Data Processing: Python Parsing and CSV Conversion Tips

JSON and CSV, the old enemies, proxy IP users how to choose?

Engaged in data collection of the old iron must have encountered this problem: the server spit out a variety of data formats, especially JSON this kind of nesting structure, look at the brain pain. At this time if there is a handy tool at hand, together with theipipgoof the proxy IP pool, it saves a lot of effort to deal with it.

Python's top three tricks for handling JSON

First of all, the most commonly used json library, this thing is like a Swiss army knife. To give a real case: an e-commerce platform for product details page, using theipipgoWhen proxy IP polling crawling, the returned JSON data can have 10 levels of nesting. This time it is necessary to use the recursive method:

def unpack_nested(data): for key, value in data.
    
        if isinstance(value, dict).
            unpack_nested(value)
        else: print(f"{key}
            print(f"{key}: {value}")

The second trick is pandas's json_normalize, which is particularly suitable for dealing with lists over dictionaries. For example, when grabbing social media data, the list of comments is often this structure. Remember to add in the request headeripipgoproxy authentication to avoid IP blocking by the target website.

Hidden pitfalls of CSV conversion

The easiest thing to fall into when converting is the encoding problem. Especially when dealing with multi-language data, it is recommended to convert to utf-8-sig format. Here is a tip: useipipgoThe code settings can be dynamically adjusted in the code when collecting data from different regions by the residential agent of the

encountered special characters how to do? Teach you a wild way: first build a template file in Excel, specify a good separator and text qualifier. DictWriter with csv.DictWriter quoting parameter control, more reliable than direct hard coding.

Practical: the proxy IP logs into a report

Suppose we useipipgoAPI to get the agent usage logs, the raw data looks like this:

{"node": "aws-us-west", "requests": 1420, "errors": {"timeout": 23, "auth_fail": 5}}

The processing steps are in four steps:
1. Using json.loads to parse the raw data
2. Expanding the errors dictionary to the main level
3. Calculating success rates
4. Output CSV with two decimal places

Remember to switch randomly in the capture scriptipipgoof the egress IP, which ensures data integrity and tests the stability of the proxy node.

Guidelines on demining of common problems

Q: How to convert nested JSON to flat CSV?
A: It is recommended to use json_normalize in pandas, together with the meta parameter to specify the parent field to be retained. If it is multi-level nesting, you can write a recursive expand function.

Q: What if the conversion speed is too slow?
A: Try these two methods: ① Use cchardet instead of chardet to detect encoding ② Switch to ijson stream parsing when dealing with large files. MatchipipgoThe exclusive proxy can avoid bandwidth competition from shared IPs.

Q: What role does the proxy IP play in data processing?
A: To give a practical scenario: when you need to batch verify the API return format, you can do it through theipipgoof different geographic nodes to initiate requests, both to test interface compatibility and to check geo-restriction policies.

Q: Why do you recommend ipipgo's services?
A: their agents have three major advantages: ① accurate city-level positioning ② response speed can be controlled within 200ms ③ support socks5 and http dual protocol. Especially when doing multinational data collection, it can bypass the common anti-climbing strategy.

Finally, a word of advice: data processing do not just focus on the code, the infrastructure must also keep up. Use a good tool + reliable agent, the efficiency is directly doubled. When you encounter specific problems, you can go toipipgoThe documentation center to find cases, their technical manuals are written in a thief grounded.