一:threading VS Thread
众所周知,python是支持多线程的,而且是native的线程,其中threading是对Thread模块做了包装,可以更加方面的被使用,threading模块里面主要对一些线程操作对象化了,创建了Thread的类。
使用线程有两种模式,一种是创建线程要执行的函数,把这个函数传递进Thread对象里,让它来执行,一种是直接从Thread继承,创建一个新的class,把线程执行的代码放到这个新的类里面,用例如下:
①使用Thread来实现多线程
#!/usr/bin/env python #-*- coding:utf-8 -*- import string import threading import time def threadMain(a): global count,mutex #获得线程名 threadname = threading.currentThread().getName() for x in xrange(0,int(a)): #获得锁 mutex.acquire() count += 1 #释放锁 mutex.release() print threadname,x,count time.sleep() def main(num): global count,mutex threads = [] count = 1 #创建一个锁 mutex = threading.Lock() #先创建线程对象 for x in xrange(0,num): threads.append(threading.Thread(target = threadMain,args=(10,))) for t in threads: t.start() for t in threads: t.join() if __name__ == "__main__": num = 4 main(num);
②使用threading来实现多线程
#!/usr/bin/env python #-*- coding:utf-8 -*- import threading import time class Test(threading.Thread): def __init__(self,num): threading.Thread.__init__(self): self._run_num = num def run(self): global count,mutex threadName = threading.currentThread.getName() for x in xrange(0,int(self._run_num)): mutex.acquire() count += 1 mutex.release() print threadName,x,count time.sleep(1) if __name__ == "__main__": global count,mutex threads = [] num = 4 count = 1 mutex.threading.Lock() for x in xrange(o,num): threads.append(Test(10)) #启动线程 for t in threads: t.start() #等待子线程结束 for t in threads: t.join()
二:optparser VS getopt
①使用getopt模块处理Unix模式的命令行选项
getopt模块用于抽出命令行选项和参数,也就是sys.argv,命令行选项使得程序的参数更加灵活,支持短选项模式和长选项模式
例:python scriptname.py –f “hello” –directory-prefix=”/home” –t --format ‘a'‘b'
getopt函数的格式:getopt.getopt([命令行参数列表],‘短选项',[长选项列表])
其中短选项名后面的带冒号(:)表示该选项必须有附加的参数
长选项名后面有等号(=)表示该选项必须有附加的参数
返回options以及args
options是一个参数选项及其value的元组((‘-f','hello'),(‘-t',''),(‘—format',''),(‘—directory-prefix','/home'))
args是除去有用参数外其他的命令行 输入(‘a',‘b')
#!/usr/bin/env python # -*- coding:utf-8 -*- import sys import getopt def Usage(): print "Usage: %s [-a|-0|-c] [--help|--output] args..."%sys.argv[0] if __name__ == "__main__": try: options,args = getopt.getopt(sys.argv[1:],"ao:c",['help',"putput="]): print options print "\n" print args for option,arg in options: if option in ("-h","--help"): Usage() sys.exit(1) elif option in ('-t','--test'): print "for test option" else: print option,arg except getopt.GetoptError: print "Getopt Error" Usage() sys.exit(1)
②optparser模块
#!/usr/bin/env python # -*- coding:utf-8 -*- import optparser def main(): usage = "Usage: %prog [option] arg1,arg2..." parser = OptionParser(usage=usage) parser.add_option("-v","--verbose",action="store_true",dest="verbose",default=True,help="make lots of noise [default]") parser.add_option("-q","--quiet",action="store_false",dest="verbose",help="be vewwy quiet (I'm hunting wabbits)") parser.add_option("-f","--filename",metavar="FILE",help="write output to FILE") parser.add_option("-m","--mode",default="intermediate",help="interaction mode: novice, intermediate,or expert [default: %default]") (options,args) = parser.parse_args() if len(args) != 1: parser.error("incorrect number of arguments") if options.verbose: print "reading %s..." %options.filename if __name__ == "__main__": main()
以上就是threading VS Thread、optparser VS getopt 的相互比较,希望对大家学习模块有所帮助。

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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