


This article brings you a simple analysis of UDP socket communication in python (with code). It has certain reference value. Friends in need can refer to it. I hope It will help you.
UDPserver.py
import socket #导入套接字模块 s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM) # - socket.AF_INET:IPV4 # - socket.STREAM:TCP # - socket.DGRAM:UDP s.bind(('',25555)) #绑定套接字有效地址和端口 #''空位任何地址 本地的127.0.0.1 和局域网还有自己真实的ip print('[+] Server Open.....') while True: try: data,c_addr = s.recvfrom(1024) #一次性接受1024bytes的数据 ,返回一个元组,其中有数据和地址 #UDP不需要构成连接,直接发送即可 print('from:',c_addr) #c_addr是一个地址,发送消息的客户端的IP和端口的二元组 print('say:%s'%(data.decode('utf-8'))) msg = data.decode('utf-8') s.sendto(msg.encode('utf-8'),c_addr) #发送信息,其中有两个参数,一个是信息,一个是目标地址和端口 except KeyboardInterrupt: break print('[+] Server Close......') s.close
UDPclient.py
import socket #导入套接字模块 c = socket.socket(socket.AF_INET,socket.SOCK_DGRAM) # - socket.AF_INET:IPV4 # - socket.STREAM:TCP # - socket.DGRAM:UDP while True: try: msg = input('>>>') if msg == 0: #判断输入是否为空 就是直接回车了 continue #UDP不需要构成连接,直接发送即可 c.sendto(msg.encode('utf-8'),('127.0.0.1',25555)) #发送消息,其中两个参数,第一个是要发送的信息 #第二个是发送的ip地址和端口,是一个元组 data,s_addr = c.recvfrom(1024) #c_addr是一个地址,发送消息的客户端的IP和端口的二元组 print('$: %s'%(data.decode('utf-8'))) except KeyboardInterrupt: break c.close()
UDP does not need to be composed Connect and send directly
Server model:
data,c_addr = s.recvfrom(1024)
s.sendto(msg, c_addr)
Messages sent by others are sent directly to the s server socket
data: sent data
c_addr: who sent it
s = socket .socket(socket.AF_INET,socket.SOCK_DGRAM)
s.bind(('',25555))
send recv
sendto recvfrom
They are all blocked
The above is the detailed content of Simple analysis of UDP socket communication in python (with code). For more information, please follow other related articles on the PHP Chinese website!

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.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

Atom editor mac version download
The most popular open source editor