1.在Scrapy工程下新建“middlewares.py”
# Importing base64 library because we'll need it ONLY in case if the proxy we are going to use requires authentication import base64 # Start your middleware class class ProxyMiddleware(object): # overwrite process request def process_request(self, request, spider): # Set the location of the proxy request.meta['proxy'] = "http://YOUR_PROXY_IP:PORT" # Use the following lines if your proxy requires authentication proxy_user_pass = "USERNAME:PASSWORD" # setup basic authentication for the proxy encoded_user_pass = base64.encodestring(proxy_user_pass) request.headers['Proxy-Authorization'] = 'Basic ' + encoded_user_pass
2.在项目配置文件里(./project_name/settings.py)添加
DOWNLOADER_MIDDLEWARES = { 'scrapy.contrib.downloadermiddleware.httpproxy.HttpProxyMiddleware': 110, 'project_name.middlewares.ProxyMiddleware': 100, }
只要两步,现在请求就是通过代理的了。测试一下^_^
from scrapy.spider import BaseSpider from scrapy.contrib.spiders import CrawlSpider, Rule from scrapy.http import Request class TestSpider(CrawlSpider): name = "test" domain_name = "whatismyip.com" # The following url is subject to change, you can get the last updated one from here : # http://www.whatismyip.com/faq/automation.asp start_urls = ["http://xujian.info"] def parse(self, response): open('test.html', 'wb').write(response.body)
3.使用随机user-agent
默认情况下scrapy采集时只能使用一种user-agent,这样容易被网站屏蔽,下面的代码可以从预先定义的user- agent的列表中随机选择一个来采集不同的页面
在settings.py中添加以下代码
DOWNLOADER_MIDDLEWARES = { 'scrapy.contrib.downloadermiddleware.useragent.UserAgentMiddleware' : None, 'Crawler.comm.rotate_useragent.RotateUserAgentMiddleware' :400 }
注意: Crawler; 是你项目的名字 ,通过它是一个目录的名称 下面是蜘蛛的代码
#!/usr/bin/python #-*-coding:utf-8-*- import random from scrapy.contrib.downloadermiddleware.useragent import UserAgentMiddleware class RotateUserAgentMiddleware(UserAgentMiddleware): def __init__(self, user_agent=''): self.user_agent = user_agent def process_request(self, request, spider): #这句话用于随机选择user-agent ua = random.choice(self.user_agent_list) if ua: request.headers.setdefault('User-Agent', ua) #the default user_agent_list composes chrome,I E,firefox,Mozilla,opera,netscape #for more user agent strings,you can find it in http://www.useragentstring.com/pages/useragentstring.php user_agent_list = [\ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1"\ "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11",\ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",\ "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",\ "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",\ "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5",\ "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5",\ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",\ "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",\ "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",\ "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",\ "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24",\ "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24" ]

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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