一、引言
Python 爬虫是一种强大的数据采集工具，它可以帮助我们从互联网上自动获取大量有价值的信息。在这篇文章中，我们将深入探讨 Python 爬虫的高级技术，包括并发处理、反爬虫策略应对、数据存储与处理等方面。通过实际的代码示例和详细的解释，读者将能够掌握更高级的爬虫技巧，提升爬虫的效率和稳定性。
二、高级爬虫技术
import asyncio
import aiohttp
async def fetch(url):
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
return await response.text()
async def main():
urls = ['https://example.com/page1', 'https://example.com/page2', 'https://example.com/page3']
tasks = [fetch(url) for url in urls]
results = await asyncio.gather(*tasks)
for result in results:
print(result)
if __name__ == '__main__':
asyncio.run(main())
import requests
from PIL import Image
import pytesseract
def handle_captcha(image_url):
response = requests.get(image_url)
with open('captcha.jpg', 'wb') as f:
f.write(response.content)
image = Image.open('captcha.jpg')
captcha_text = pytesseract.image_to_string(image)
return captcha_text
def simulate_login(username, password):
session = requests.Session()
login_url = 'https://example.com/login'
data = {
'username': username,
'password': password
}
response = session.post(login_url, data=data)
# 检查登录是否成功
if response.status_code == 200:
return session
else:
return None
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('sqlite:///data.db')
def save_data_to_db(data):
df = pd.DataFrame(data)
df.to_sql('data_table', con=engine, if_exists='append', index=False)
def process_data():
df = pd.read_sql_query('SELECT * FROM data_table', con=engine)
# 进行数据清洗和预处理
cleaned_df = df.dropna()
# 进行数据分析
analysis_result = cleaned_df.describe()
print(analysis_result)
三、实战应用
import requests
from bs4 import BeautifulSoup
def scrape_product_info(url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
product_name = soup.find('h1', class_='product-name').text
price = soup.find('span', class_='price').text
rating = soup.find('div', class_='rating').text
return {
'product_name': product_name,
'price': price,
'rating': rating
}
def scrape_ecommerce_site():
base_url = 'https://example.com/products'
page = 1
while True:
url = f'{base_url}?page={page}'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
products = soup.find_all('div', class_='product')
if not products:
break
for product in products:
product_info = scrape_product_info(product['href'])
save_data_to_db(product_info)
page += 1
import requests
from bs4 import BeautifulSoup
def scrape_article_info(url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('h1', class_='article-title').text
content = soup.find('div', class_='article-content').text
publish_time = soup.find('span', class_='publish-time').text
return {
'title': title,
'content': content,
'publish_time': publish_time
}
def scrape_news_site():
base_url = 'https://example.com/news'
response = requests.get(base_url)
soup = BeautifulSoup(response.text, 'html.parser')
articles = soup.find_all('a', class_='article-link')
for article in articles:
article_url = article['href']
article_info = scrape_article_info(article_url)
save_data_to_db(article_info)
四、总结
通过本文的学习，我们掌握了 Python 爬虫的高级技术，包括并发处理、反爬虫策略应对、数据存储与处理等方面。在实战应用中，我们通过爬取电商网站商品信息和新闻网站文章内容，进一步巩固了所学的知识。希望读者能够在实际项目中灵活运用这些技术，开发出高效、稳定的爬虫程序。
请注意，在实际应用中，爬虫行为需要遵守法律法规和网站的使用规则，避免对网站造成不必要的负担和法律风险。
以上内容仅供学习参考，实际使用时请根据具体情况进行调整和优化。
本文部分代码转自：https://www.wodianping.com/app/2024-10/37518.html
标签：实战,soup,Python,text,爬虫,url,data,response From： https://www.cnblogs.com/wodianpingcom/p/18447272