Extract structured data using Python&#s advanced techniques

In the data-driven era, extracting structured data from multiple sources such as web pages, APIs, and databases has become a critical foundation for data analysis, machine learning, and business decision-making. Python's rich library and strong community support have made it the leading language for data extraction tasks. In this article, we will explain in detail how to extract structured data efficiently and accurately using advanced Python techniques, and briefly explain the supporting role of 98IP Proxy in the data crawling process. touch

I. Basics of data crawling

1.1 Request and Response

The first step in data crawling is typically sending an HTTP request to the target website and receiving the returned HTML or JSON response. Python's requests library simplifies this process.

import requests

url = 'http://example.com'
response = requests.get(url)
html_content = response.text

1.2 Parsing HTML

Parse the HTML document and extract the data you need using libraries like BeautifulSoup and lxml. For example, extract all article titles.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')
titles = [title.text for title in soup.find_all('h2', class_='article-title')]

II. Handling complex web page structures

2.1 Processing JavaScript rendering using Selenium

For web pages that rely on JavaScript to load content dynamically, Selenium provides a browser automation solution.

from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get('http://example.com')

# JavaScriptの読み込み完了を待つ
# ...(明示的または暗黙的に待機する必要がある場合があります)
titles = [element.text for element in driver.find_elements(By.CSS_SELECTOR, '.article-title')]
driver.quit()

2.2 Dealing with anti-crawling mechanisms

Websites may use various anti-crawling mechanisms, such as verification codes, IP blocks, etc. You can avoid IP blocking by using a proxy IP (such as 98IP proxy).

proxies = {
    'http': 'http://proxy.98ip.com:port',
    'https': 'https://proxy.98ip.com:port',
}

response = requests.get(url, proxies=proxies)

III. Data cleansing and transformation

3.1 Data cleansing

Extracted data often contains noise, such as null values, duplicate values, and mismatched formats. We use the Pandas library to do data cleansing.

import pandas as pd

df = pd.DataFrame(titles, columns=['

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