The examples in this article describe common design patterns in python. Share it with everyone for your reference, the details are as follows:
# #!/usr/bin/env python # # -*- coding:utf-8 # # class HttpBase: # def get(self): # psss # class Http1(HttpBase): # def get(self): # print 'http1' # class Http2(HttpBase): # def get(self): # print 'http2' # # # class Base: # def __init__(self): # self.httpobj = None # def http(self): # self.httpobj.get() # def compute(self): # self.http() # self.show() # #虚函数 # def show(self): # pass # def notify(self, k): # print 'notify', k # # # #桥接模式,通过A,B 关联不同的http1和http2 # class BaseA(Base): # def __init__(self): # self.httpobj = Http1() # def notify(self, k): # print 'A notify', k # def show(self): # print 'show a' # # class BaseB(Base): # def __init__(self): # self.httpobj = Http2() # def notify(self, k): # print 'B notify', k # def show(self): # print 'show b' # # #观测者模式 # class Observer: # def __init__(self): # self.listOB = [] # def register(self, obj): # self.listOB.append(obj) # def notify(self): # for obj in self.listOB: # obj.notify(len(self.listOB)) # # #适配器模式 # class B1: # def http(self): # BaseB().http() # #工厂模式 # class Factory: # def CreateA(self): # return BaseA() # def CreateB(self): # return BaseB() # # # #单例模式 # class Logger(object): # log = None # @staticmethod # def new(): # # import threading # #线程安全 # mylock = threading.RLock() # mylock.acquire() # if not Logger.log: # Logger.log = Logger() # mylock.release() # # return Logger.log # def write(self, v): # print 'Logger ', v # # if __name__ == "__main__": # a = Factory().CreateA() # b = Factory().CreateB() # # objS = Observer() # objS.register(a) # objS.register(b) # # a.compute() # b.compute() # objS.notify() # # b1 = B1() # b1.http() # # Logger.new().log.write('v')
Readers who are interested in more Python-related content can check out the special topics on this site: "Summary of Python Image Operation Skills", "Python Data Structure and Algorithm Tutorial", "Python Socket Programming Skills Summary", "Python Function Using Skills Summary" ", "Python String Operation Skills Summary", "Python Introduction and Advanced Classic Tutorial" and "Python File and Directory Operation Skills Summary"
I hope this article will be helpful to everyone in Python programming.

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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.


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