首页 前端知识 Python爬虫技术 第14节 HTML结构解析

Python爬虫技术 第14节 HTML结构解析

2024-08-10 00:08:41 前端知识 前端哥 331 162 我要收藏

HTML 结构解析是 Web 爬虫中的核心技能之一,它允许你从网页中提取所需的信息。Python 提供了几种流行的库来帮助进行 HTML 解析,其中最常用的是 BeautifulSouplxml

在这里插入图片描述

1. 安装必要的库

首先,你需要安装 requests(用于发送 HTTP 请求)和 beautifulsoup4(用于解析 HTML)。可以通过 pip 安装:

pip install requests beautifulsoup4

2. 发送 HTTP 请求并获取 HTML 内容

使用 requests 库可以轻松地从网站抓取 HTML 页面:

import requests

url = "https://www.example.com"
response = requests.get(url)

# 检查请求是否成功
if response.status_code == 200:
    html_content = response.text
else:
    print(f"Failed to retrieve page, status code: {response.status_code}")

3. 解析 HTML 内容

接下来,使用 BeautifulSoup 解析 HTML 内容:

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')

这里的 'html.parser' 是解析器的名字,BeautifulSoup 支持多种解析器,包括 Python 自带的标准库、lxmlhtml5lib

4. 选择和提取信息

一旦你有了 BeautifulSoup 对象,你可以开始提取信息。以下是几种常见的选择器方法:

  • 通过标签名

    titles = soup.find_all('h1')
    
  • 通过类名

    articles = soup.find_all('div', class_='article')
    
  • 通过 ID

    main_content = soup.find(id='main-content')
    
  • 通过属性

    links = soup.find_all('a', href=True)
    
  • 组合选择器

    article_titles = soup.select('div.article h2.title')
    

5. 遍历和处理数据

提取到数据后,你可以遍历并处理它们:

for title in soup.find_all('h2'):
    print(title.text.strip())

6. 递归解析

对于复杂的嵌套结构,你可以使用递归函数来解析:

def parse_section(section):
    title = section.find('h2')
    if title:
        print(title.text.strip())

    sub_sections = section.find_all('section')
    for sub_section in sub_sections:
        parse_section(sub_section)

sections = soup.find_all('section')
for section in sections:
    parse_section(section)

7. 实战示例

让我们创建一个完整的示例,抓取并解析一个简单的网页:

import requests
from bs4 import BeautifulSoup

url = "https://www.example.com"

# 发送请求并解析 HTML
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

# 找到所有的文章标题
article_titles = soup.find_all('h2', class_='article-title')

# 输出所有文章标题
for title in article_titles:
    print(title.text.strip())

这个示例展示了如何从网页中抓取所有具有 class="article-title"h2 元素,并打印出它们的文本内容。

以上就是使用 Python 和 BeautifulSoup 进行 HTML 结构解析的基本流程。当然,实际应用中你可能需要处理更复杂的逻辑,比如处理 JavaScript 渲染的内容或者分页等。

在我们已经讨论的基础上,让我们进一步扩展代码,以便处理更复杂的场景,比如分页、错误处理、日志记录以及数据持久化。我们将继续使用 requestsBeautifulSoup,并引入 loggingsqlite3 来记录日志和存储数据。

1. 异常处理和日志记录

在爬取过程中,可能会遇到各种问题,如网络错误、服务器错误或解析错误。使用 try...except 块和 logging 模块可以帮助我们更好地处理这些问题:

import logging
import requests
from bs4 import BeautifulSoup

logging.basicConfig(filename='crawler.log', level=logging.INFO, format='%(asctime)s:%(levelname)s:%(message)s')

def fetch_data(url):
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raises an HTTPError for bad responses
        soup = BeautifulSoup(response.text, 'html.parser')
        return soup
    except requests.exceptions.RequestException as e:
        logging.error(f"Failed to fetch {url}: {e}")
        return None

# Example usage
url = 'https://www.example.com'
soup = fetch_data(url)
if soup:
    # Proceed with parsing...
else:
    logging.info("No data fetched, skipping...")

2. 分页处理

许多网站使用分页显示大量数据。你可以通过检查页面源码找到分页链接的模式,并编写代码来遍历所有页面:

def fetch_pages(base_url, page_suffix='page/'):
    current_page = 1
    while True:
        url = f"{base_url}{page_suffix}{current_page}"
        soup = fetch_data(url)
        if not soup:
            break
        # Process page data here...

        # Check for next page link
        next_page_link = soup.find('a', text='Next')
        if not next_page_link:
            break
        current_page += 1

3. 数据持久化:SQLite

使用数据库存储爬取的数据可以方便后续分析和检索。SQLite 是一个轻量级的数据库,非常适合小型项目:

import sqlite3

def init_db():
    conn = sqlite3.connect('data.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS articles (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            title TEXT NOT NULL,
            author TEXT,
            published_date DATE
        )
    ''')
    conn.commit()
    return conn

def save_article(conn, title, author, published_date):
    cursor = conn.cursor()
    cursor.execute('''
        INSERT INTO articles (title, author, published_date) VALUES (?, ?, ?)
    ''', (title, author, published_date))
    conn.commit()

# Initialize database
conn = init_db()

# Save data
save_article(conn, "Example Title", "Author Name", "2024-07-24")

4. 完整示例:抓取分页数据并保存到 SQLite

让我们将上述概念整合成一个完整的示例,抓取分页数据并将其保存到 SQLite 数据库:

import logging
import requests
from bs4 import BeautifulSoup
import sqlite3

logging.basicConfig(filename='crawler.log', level=logging.INFO)

def fetch_data(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        return BeautifulSoup(response.text, 'html.parser')
    except requests.exceptions.RequestException as e:
        logging.error(f"Failed to fetch {url}: {e}")
        return None

def fetch_pages(base_url, page_suffix='page/'):
    conn = sqlite3.connect('data.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS articles (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            title TEXT NOT NULL,
            author TEXT,
            published_date DATE
        )
    ''')
    conn.commit()

    current_page = 1
    while True:
        url = f"{base_url}{page_suffix}{current_page}"
        soup = fetch_data(url)
        if not soup:
            break

        # Assume the structure of the site allows us to find titles easily
        titles = soup.find_all('h2', class_='article-title')
        for title in titles:
            save_article(conn, title.text.strip(), None, None)

        next_page_link = soup.find('a', text='Next')
        if not next_page_link:
            break
        current_page += 1

    conn.close()

def save_article(conn, title, author, published_date):
    cursor = conn.cursor()
    cursor.execute('''
        INSERT INTO articles (title, author, published_date) VALUES (?, ?, ?)
    ''', (title, author, published_date))
    conn.commit()

# Example usage
base_url = 'https://www.example.com/articles/'
fetch_pages(base_url)

这个示例将抓取 https://www.example.com/articles/ 上的分页数据,保存文章标题到 SQLite 数据库。注意,你需要根据实际网站的 HTML 结构调整 find_allfind 方法的参数。

既然我们已经有了一个基本的框架来抓取分页数据并存储到 SQLite 数据库中,现在让我们进一步完善这个代码,包括添加更详细的错误处理、日志记录、以及处理动态加载的网页内容(通常由 JavaScript 渲染)。

1. 更详细的错误处理

fetch_data 函数中,除了处理请求错误之外,我们还可以捕获和记录其他可能发生的错误,比如解析 HTML 的错误:

def fetch_data(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        return soup
    except requests.exceptions.RequestException as e:
        logging.error(f"Request error fetching {url}: {e}")
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}")
    return None

2. 更详细的日志记录

在日志记录方面,我们可以增加更多的信息,比如请求的 HTTP 状态码、响应时间等:

import time

def fetch_data(url):
    try:
        start_time = time.time()
        response = requests.get(url)
        elapsed_time = time.time() - start_time
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        logging.info(f"Fetched {url} successfully in {elapsed_time:.2f} seconds, status code: {response.status_code}")
        return soup
    except requests.exceptions.RequestException as e:
        logging.error(f"Request error fetching {url}: {e}")
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}")
    return None

3. 处理动态加载的内容

当网站使用 JavaScript 动态加载内容时,普通的 HTTP 请求无法获取完整的内容。这时可以使用 SeleniumPyppeteer 等库来模拟浏览器行为。这里以 Selenium 为例:

from selenium import webdriver
from selenium.webdriver.chrome.options import Options

def fetch_data_with_js(url):
    options = Options()
    options.headless = True  # Run Chrome in headless mode
    driver = webdriver.Chrome(options=options)
    driver.get(url)
    
    # Add wait time or wait for certain elements to load
    time.sleep(3)  # Wait for dynamic content to load
    
    html = driver.page_source
    driver.quit()
    
    return BeautifulSoup(html, 'html.parser')

要使用这段代码,你需要先下载 ChromeDriver 并确保它在系统路径中可执行。此外,你还需要安装 selenium 库:

pip install selenium

4. 整合所有改进点

现在,我们可以将上述所有改进点整合到我们的分页数据抓取脚本中:

import logging
import time
import requests
from bs4 import BeautifulSoup
import sqlite3
from selenium import webdriver
from selenium.webdriver.chrome.options import Options

logging.basicConfig(filename='crawler.log', level=logging.INFO)

def fetch_data(url):
    try:
        start_time = time.time()
        response = requests.get(url)
        elapsed_time = time.time() - start_time
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        logging.info(f"Fetched {url} successfully in {elapsed_time:.2f} seconds, status code: {response.status_code}")
        return soup
    except requests.exceptions.RequestException as e:
        logging.error(f"Request error fetching {url}: {e}")
    except Exception as e:
        logging.error(f"An unexpected error occurred: {e}")
    return None

def fetch_data_with_js(url):
    options = Options()
    options.headless = True
    driver = webdriver.Chrome(options=options)
    driver.get(url)
    time.sleep(3)
    html = driver.page_source
    driver.quit()
    return BeautifulSoup(html, 'html.parser')

def fetch_pages(base_url, page_suffix='page/', use_js=False):
    conn = sqlite3.connect('data.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS articles (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            title TEXT NOT NULL,
            author TEXT,
            published_date DATE
        )
    ''')
    conn.commit()

    current_page = 1
    fetch_function = fetch_data_with_js if use_js else fetch_data

    while True:
        url = f"{base_url}{page_suffix}{current_page}"
        soup = fetch_function(url)
        if not soup:
            break

        titles = soup.find_all('h2', class_='article-title')
        for title in titles:
            save_article(conn, title.text.strip(), None, None)

        next_page_link = soup.find('a', text='Next')
        if not next_page_link:
            break
        current_page += 1

    conn.close()

def save_article(conn, title, author, published_date):
    cursor = conn.cursor()
    cursor.execute('''
        INSERT INTO articles (title, author, published_date) VALUES (?, ?, ?)
    ''', (title, author, published_date))
    conn.commit()

# Example usage
base_url = 'https://www.example.com/articles/'
use_js = True  # Set to True if the site uses JS for loading content
fetch_pages(base_url, use_js=use_js)

这个改进版的脚本包含了错误处理、详细的日志记录、以及处理动态加载内容的能力,使得它更加健壮和实用。

转载请注明出处或者链接地址:https://www.qianduange.cn//article/15129.html
评论
发布的文章

Jquery (第三章笔记)

2024-08-18 00:08:37

jquery实现tab切换简单好用

2024-08-18 00:08:35

jQuery Cookie 插件使用教程

2024-08-14 22:08:01

jQuery的DOM操作

2024-08-18 00:08:21

echarts显示中国地图

2024-08-18 00:08:11

大家推荐的文章
会员中心 联系我 留言建议 回顶部
复制成功!