What is Data Parsing: A Guide to Field Extraction and Cleansing

What exactly is data parsing tossing around?

Engaging in data parsing is like panning for gold in the garbage, you have to pick out the phone number in the shredded paper, and then wipe the greasy courier sheets clean. For example, when using proxy IP to capture the price of e-commerce, we often encounter product information wrapped in advertising code, and then we have toLike tweezers on a strand of hair.Pick out the key fields like price and inventory.

Three axes for field extraction

Here to teach the guys a few earth methods, guaranteed to work better than the textbook:

1. Don't memorize regular expressions: encounter a price grab, directly use thed+.d{2}This string of cardinal characters is much faster than memorizing formulas

import re
price = re.search(r'¥(d+.d{2})', html).group(1)

2. CSS selector lazy method: Right-click "Copy selector" with browser developer tools, and it's a snap!

3. The Great Eye Calibration MethodAfter grabbing the data, remember to use ipipgo's proxy IP to change a regional IP to re-visit and compare the data to see if it is consistent.

Five Steps to a Data Bath

Dirty data is like mashed potatoes, it has to be washed in this process:

How proxy IPs act as scavengers

There are two great tricks for doing data cleansing with ipipgo's proxy IP:

1. Exception data review: When a batch of data is found to be abnormal, immediately switch the proxy IP to re-request, to exclude false data caused by IP blocking.

2. Geographic calibrationFor example, when crawling oil price information, use proxy IPs of different regions to obtain real regional data to avoid interference by the website's anti-climbing mechanism.

A practical guide to avoiding the pit

Recently, a customer used our ipipgo's residential proxy to crawl a certain clothing website with old data loss. Later it was found to be:

- No timeout retry mechanism.
- Anti-Crawler Trap Links for Unfiltered Sites
Change the following and you'll see immediate results:

retries = 3
while retries.
    try: response = requests.get(url, proxies=ipipgo_proxy)
        response = requests.get(url, proxies=ipipgo_proxy)
        break: response = requests.get(url), proxies=ipipgo_proxy)
    except: response = requests.get(url), proxies=ipipgo_proxy
        time.sleep(2retries)
        time.sleep(2retries)

question-and-answer session

Q: Why do I need a proxy IP to clean my data?
A: Just like washing a car can not always use the same bucket of water, continue to use the same IP request is easy to be blocked, ipipgo's dynamic IP pool can ensure the consistency of the data collection

Q: What should I do if the fields are always incomplete?
A: First check the web page structure changes, and then use different regions of the proxy IP access test. Last time a customer with our Hong Kong node suddenly can not get the price, change to the U.S. node is normal!

Q: What are the advantages of ipipgo over others?
A: Our home IP pool updates 20% IP address every hour, especially suitable for scenarios that require long-term data monitoring. Just like flowing water does not rot, always change new

Say something from the heart.

Data cleaning this thing, three points rely on technology and seven points rely on tools. The last time I saw a buddy to build their own proxy server, the results of cleaning data IP was blocked to mom do not recognize. Later changed to ipipgo short-effect proxy, with automatic switching function, the efficiency directly doubled. Remember.A good knife is used on its blade.The professional is better off leaving the professional tools to the professional.