15分钟学 Python 第31天 :Web Scraping

Day 31:Web Scraping

1. Web Scraping 概述

Web Scraping(网页抓取)是一种自动提取网站数据的技术。它常用于从网页中收集信息,对数据进行分析和处理。无论是获取产品价格、市场调研,还是收集新闻信息,Web Scraping都有着广泛的应用。

1.1 Web Scraping 的应用场景
应用场景说明
数据采集定期从网站提取最新数据
媒体内容抓取收集新闻文章、博客文章
价格监控追踪产品价格,竞争对手的动态
市场研究收集消费者评论、产品信息
研究数据收集集合用于科学研究或分析的数据

2. Web Scraping 的工具

进行Web Scraping需要一些工具和库,以下是Python中常用的几个库:

库名作用
Requests处理HTTP请求,获取网站HTML内容
Beautiful Soup解析HTML和XML,提取数据
lxml更高效的HTML/XML解析库
Scrapy完整的Web Scraping框架
Selenium自动化浏览器操作,抓取动态网页内容

3. 使用 Requests 和 Beautiful Soup 进行Web Scraping

3.1 安装所需库

首先,确保您已安装requestsbeautifulsoup4库。可以使用以下命令安装:

pip install requests beautifulsoup4
3.2 基本流程

进行Web Scraping的一般步骤如下:

  1. 使用Requests库获取网页内容。
  2. 使用Beautiful Soup解析网页。
  3. 提取所需的数据。
  4. 保存数据(例如,存入CSV文件、数据库等)。

4. 示例代码

以下是一个简单的Web Scraping示例,抓取一个示例网站的标题和链接。

4.1 示例网站

假设我们要抓取以下网站的数据:

示例网站http://quotes.toscrape.com/

4.2 示例代码
import requests
from bs4 import BeautifulSoup

# 1. 发送HTTP请求并获取网页内容
url = 'http://quotes.toscrape.com/'
response = requests.get(url)

# 检查请求是否成功
if response.status_code == 200:
    # 2. 解析网页内容
    soup = BeautifulSoup(response.text, 'html.parser')

    # 3. 提取所需数据
    quotes = soup.find_all('div', class_='quote')
    
    # 存储结果
    result = []
    
    for quote in quotes:
        text = quote.find('span', class_='text').get_text()
        author = quote.find('small', class_='author').get_text()
        result.append({'text': text, 'author': author})

    # 4. 打印提取的数据
    for item in result:
        print(f"Quote: {item['text']} - Author: {item['author']}")
else:
    print(f"Failed to retrieve the page. Status code: {response.status_code}")
4.3 代码运行流程图

以下是该示例代码的运行流程图:

+-------------------+
| 发送HTTP请求      |
| 获取网页内容     |
+---------+---------+
          |
          v
+---------+---------+
| 解析网页内容     |
+---------+---------+
          |
          v
+---------+---------+
| 提取所需的数据   |
+---------+---------+
          |
          v
+---------+---------+
| 打印或保存数据   |
+-------------------+

5. 处理复杂情况

5.1 动态网页

对于JavaScript生成的动态网页,使用Selenium库更为合适,因为它可以操控浏览器以模拟用户操作。

5.1.1 安装 Selenium
pip install selenium
5.1.2 示例代码
from selenium import webdriver
from selenium.webdriver.common.by import By

# 启动浏览器
driver = webdriver.Chrome()  # 确保您已安装Chrome浏览器和对应的ChromeDriver

# 访问网站
driver.get('http://quotes.toscrape.com/js/')

# 找到元素并提取数据
quotes = driver.find_elements(By.CLASS_NAME, 'quote')

for quote in quotes:
    text = quote.find_element(By.CLASS_NAME, 'text').text
    author = quote.find_element(By.CLASS_NAME, 'author').text
    print(f"Quote: {text} - Author: {author}")

# 关闭浏览器
driver.quit()

6. 常见问题及最佳实践

  • 避免过于频繁的请求:向同一个网站发送过多请求可能被服务器屏蔽。建议使用time.sleep()函数设置请求间隔。

  • 使用代理:通过使用代理来进行Scraping可以避免IP被封。

  • 遵守robots.txt:在抓取之前查看网站的robots.txt文件,确保您的行为没有违反网站政策。

7. 练习题

  1. 使用Requests和Beautiful Soup抓取另一种类型的网站数据(如电影网站的电影评分和评价)。
  2. 将抓到的数据保存为CSV文件。
  3. 试着使用Selenium抓取具有动态加载内容的网站。

8. 总结

Web Scraping是一项强大的技能,能帮助您从网络中收集和分析数据。通过掌握Requests和Beautiful Soup等工具,您可以高效地获取所需信息。记住在使用Web Scraping时要遵守相关法律法规和网站的规定,维护良好的网络环境。


在这里插入图片描述

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Python Web Scraping - Second Edition by Katharine Jarmul English | 30 May 2017 | ASIN: B0725BCPT1 | 220 Pages | AZW3 | 3.52 MB Key Features A hands-on guide to web scraping using Python with solutions to real-world problems Create a number of different web scrapers in Python to extract information This book includes practical examples on using the popular and well-maintained libraries in Python for your web scraping needs Book Description The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics. What you will learn Extract data from web pages with simple Python programming Build a concurrent crawler to process web pages in parallel Follow links to crawl a website Extract features from the HTML Cache downloaded HTML for reuse Compare concurrent models to determine the fastest crawler Find out how to parse JavaScript-dependent websites Interact with forms and sessions About the Author Katharine Jarmul is a data scientist and Pythonista based in Berlin, Germany. She runs a data science consulting company, Kjamistan, that provides services such as data extraction, acquisition, and modelling for small and large companies. She has been writing Python since 2008 and scraping the web with Python since 2010, and has worked at both small and large start-ups who use web scraping for data analysis and machine learning. When she's not scraping the web, you can follow her thoughts and activities via Twitter (@kjam) Richard Lawson is from Australia and studied Computer Science at the University of Melbourne. Since graduating, he built a business specializing in web scraping while travelling the world, working remotely from over 50 countries. He is a fluent Esperanto speaker, conversational in Mandarin and Korean, and active in contributing to and translating open source software. He is currently undertaking postgraduate studies at Oxford University and in his spare time enjoys developing autonomous drones. Table of Contents Introduction Scraping the data Caching downloads Concurrent downloading Dynamic content Interacting with forms Solving CAPTCHA Scrapy Putting it All Together
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