由于工作的需求,需要用python做一个类似网络爬虫的采集器。虽然Python的urllib模块提供更加方便简洁操作,但是涉及到一些底层的需求,如手动设定User-Agent,Referer等,所以选择了直接用socket进行设计。当然,这样的话,需要对HTTP协议比较熟悉,HTTP协议这里就不做讲解了。整个python的代码如下:
#!/usr/bin env python import socket host="www.baidu.com" se=socket.socket(socket.AF_INET,socket.SOCK_STREAM) se.connect((host,80)) se.send("GET / HTTP/1.1\n") se.send("Accept:text/html,application/xhtml+xml,*/*;q=0.8\n") #se.send("Accept-Encoding:gzip,deflate,sdch\n") se.send("Accept-Language:zh-CN,zh;q=0.8,en;q=0.6\n") se.send("Cache-Control:max-age=0\n") se.send("Connection:keep-alive\n") se.send("Host:"+host+"\r\n") se.send("Referer:http://www.baidu.com/\n") se.send("user-agent: Googlebot\n\n") print se.recv(1024)
代码运行正常,但是发现一个比较重要的问题,运行结果只返回了HTTP的头部信息,网页的内容则没有被返回。网上查找了很多资料,一无所获,经过一夜的思考,突然想到了一个问题,有可能我请求的资源非常大,一个网络的IP包的大小,它是受很多因素制约的,最典型的便是MTU(最大传输单元),那么会不会我请求的数据被分割了,HTTP的头部信息只是一部分,其它数据还在传输或者缓冲区呢?于是做了这样一个遍历:
while True: buf = se.recv(1024) if not len(buf): break print buf
这样发现所有请求的数据均被返回了,看来要想做好网络编程,深入理解TCP/IP协议是非常必要的。

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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