API to CSV best method: API data to CSV tutorials

Hands on teaching you to convert API data to CSV file

Everyone should have encountered this situation when doing data capture: it's hard to get through the API interface, but the result is that the returned data is a mess and can't be used at all. This time you have to rely on theProxy IP Serviceto stabilize the data source, and then turn the data into a common format such as CSV. Today we take ipipgo proxy service as a chestnut, say how to operate.

Why do I have to use a proxy IP?

Many websites have API calls forfrequency limitIf you use your own real IP to dislike it, it will be blocked in minutes. ipipgo's dynamic residential proxy can automatically switch the export IP, and the same interface has not triggered the limit of 200 consecutive invocations. The point is that their IP pool is large enough, unlike some small workshops with a total of several hundred IP back and forth.

import requests
from ipipgo import get_proxy ipipgo official SDK

def fetch_api_data(url):
    proxy = get_proxy(type='https') Automatically fetch the latest proxies.
    headers = {'User-Agent': 'Mozilla/5.0'}
    headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, proxies={'https': proxy}, headers=headers, timeout=10)
        return response.json()
    except Exception as e.
        print(f "Request failed, changing IP automatically...") Error message: {str(e)}")
        return fetch_api_data(url) Auto-retry mechanism

Converting CSV's to real-world sets

Don't rush to convert your API data when you get it, do these three things first:

1. field cleaning: Remove unused nested fields (e.g., separate out address.city)
2. coding harmonization: All text mandatory conversion UTF-8, save open csv messy code
3. Exception handling: Set default values for fields that may be missing, e.g., 0 if there is no data in the price field.

The csv module for python is recommended, it's much lighter than pandas. Especially when dealing with millions of data, you can save half of the memory:

import csv

def json_to_csv(data, filename).
     Extract all field names
    fieldnames = list(data[0].keys())

    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        for row in data.
             Handling nested fields
            if 'location' in row.
                row['city'] = row['location'].get('city','')
                del row['location']
            writer.writerow(row)

Tried and tested tips

- IP Rotation Timing: It is recommended to change IP once every 50 data processing, so as not to waste IP resources, but also to avoid being blocked!
- timeout setting: Set the connection timeout to 3 seconds and the read timeout to 15 seconds, and switch the proxy immediately when it encounters a lag.
- Calibration of results: Randomly check 10 items after transferring the CSV, and use ipipgo's different exit IPs to request the original API to do data comparison.

Common pitfalls QA

Q：What is the messy code when I open the CSV?
A: eighty percent is a coding problem, it is recommended to write the file to force the specified encoding = 'utf-8-sig', this parameter can be compatible with the Excel

Q: The amount of data is too large for the memory to carry?
A: Change to use the generator to write one by one, don't load all the data at once. At the same time, adjust the proxy switching interval of ipipgo to avoid overloading a single IP.

Q: What if certain fields are often missing?
A: Pre-define all possible fields in fieldnames, and automatically fill in the empty strings if they are missing. Remember to enable ipipgo's request retry function, sometimes it is the network fluctuation caused by data loss!

Why ipipgo?

Used 7 or 8 proxy providers and finally locked into ipipgo because of these three things:
1. True Residential IP: not as easy to recognize as a server room agent
2. dynamic forensics: No need to manually enter account passwords, SDK takes care of it automatically!
3. precise positioning: Accurate down to the city level when specific regional IPs are required

They've recently put on a newIP Survival PredictionThe function can tell in advance how much available time is left for the current IP. For operations such as CSV transfer, which require stable connections, it is recommended to choose IP segments that have been alive for more than 30 minutes.

Lastly, I would like to remind you that after transferring the data, you should remember to use the proxy IP to verify the data again. Previously suffered a loss, the local look good CSV, the customer side said that the lack of data, and later found that some regional IP by the target site special treatment. Now use ipipgo's global node to do the second check, and no more problems.