The factory pattern is a design pattern that provides an interface for creating objects, but the instantiation process of specific objects is completed by the factory class. In Python, you can use the simple factory pattern or the Factory method pattern pattern to implement the factory pattern. The following is an example code that uses the simple factory mode: ```python class Dog: def __init__(self, name): self.name = name def speak(self): return "Woof!" class Cat: def __init__(self, name): self.name = name def speak(self): return "Meow!" class AnimalFactory: def create_animal(self, animal_type, name): if animal_type == "dog": return Dog(name) elif animal_type == "cat": return Cat(name) else: raise ValueError("Invalid animal type.") factory = AnimalFactory() animal1 = factory.create_animal("dog", "Buddy") animal2 = factory.create_animal("cat", "Milo") print(animal1.name) # Output: Buddy print(animal1.speak()) # Output: Woof! print(animal2.name) # Output: Milo print(animal2.speak()) # Output: Meow! ``` In the above code, we defined two specific animal classes, 'Dog' and 'Cat', and both implemented the 'speak' method` The AnimalFactory class is a simple factory class that has a 'create'_ The 'animal' method, based on the parameter 'animal'_ Type 'to determine which specific animal object to create. By calling 'factory. create'_ The 'animal' method allows us to create different types of animal objects as needed. In this way, we can encapsulate the process of creating objects in the factory class, and users only need to call the methods of the factory class without worrying about the instantiation process of specific objects. This is a simple example of using Python to implement the factory pattern. In practical applications, the factory pattern can provide a more flexible way to create objects, and can well follow the Open–closed principle.

There are many ways to implement Singleton pattern in Python, and the following two common methods are given. 1. Use module level variables: ```python class Singleton: Single instance class def __new__(cls, *args, **kwargs): if not hasattr(cls, '_instance'): cls._instance = super().__new__(cls, *args, **kwargs) return cls._instance #Usage examples obj1 = Singleton() obj2 = Singleton() print(obj1 is obj2) # True ``` 2. Use decorators: ```python def singleton(cls): instances = {} def wrapper(*args, **kwargs): if cls not in instances: instances[cls] = cls(*args, **kwargs) return instances[cls] return wrapper @singleton class Singleton: Single instance class pass #Usage examples obj1 = Singleton() obj2 = Singleton() print(obj1 is obj2) # True ``` Both of the above methods can implement the Singleton pattern to ensure that there is only one instance in the program. The implementation principle is through rewriting`__ New__` Method to control the instantiation process and ensure that only one instance is created.

The Observer pattern is a behavioral design pattern used to establish one to many dependencies between objects. When an object changes state, all objects that depend on it will be notified and automatically updated. In Python, you can use the built-in Observer pattern implementation. The following is a simple example code to demonstrate how to use Python to implement the Observer pattern: ```python class Subject: def __init__(self): self._observers = [] def attach(self, observer): self._observers.append(observer) def detach(self, observer): self._observers.remove(observer) def notify(self, message): for observer in self._observers: observer.update(message) class Observer: def __init__(self, name): self._name = name def update(self, message): print(f"{self._name} received message: {message}") #Create Theme Object subject = Subject() #Creating Observer Objects observer1 = Observer("Observer1") observer2 = Observer("Observer2") observer3 = Observer("Observer3") #Add Observer to Theme Object subject.attach(observer1) subject.attach(observer2) subject.attach(observer3) #Send notifications to all observers subject.notify("Hello, Observers!") #Remove an Observer subject.detach(observer2) #Send another notification to the remaining observers subject.notify("Goodbye, Observer2!") ``` In the example code above, the 'Subject' class is the subject object responsible for managing observers. It includes methods such as' attach ',' detach ', and' notify '` The 'attach' method is used to add observers to the topic object, the 'detach' method is used to remove observers, and the 'notify' method is used to send notifications to all observers. `The Observer class is an observer object that contains the 'update' method to handle received notifications. In the sample code, the Observer pattern is implemented by creating the subject and observer objects and calling the corresponding methods. When the 'notify' method is called, the subject object will sequentially call the 'update' method of each observer and pass the corresponding message. Note: Python also has a simpler implementation of the Observer pattern, using the built-in 'Observable' and 'Observer' classes. However, this method has been abandoned in Python 3, so it is not recommended to use it. It is recommended to implement the Observer pattern in the above example code.

The Adapter pattern is a structural design pattern that allows the interface of a class to be converted into another interface expected by the client. Python provides a flexible way to implement the Adapter pattern. The following is a complete example code of using Python to implement the Adapter pattern: ```python #Target interface class Target: def request(self): pass #Classes that need to be adapted class Adaptee: def specific_request(self): print("Adaptee specific_request") #Adapter Class class Adapter(Target): def __init__(self, adaptee): self.adaptee = adaptee def request(self): self.adaptee.specific_request() #Client code def client_code(target): target.request() #Creating Adapt Objects adaptee = Adaptee() adapter = Adapter(adaptee) #Client directly uses adapter client_code(adapter) ``` In the above code, we defined the Target interface and implemented the Adaptee class, which contains the specific requests we need to adapt to (specific_request). Then, we created an adapter class Adapter, which inherits from the Target interface and takes the Adaptee object as a parameter in the constructor. Finally, in the client code, we instantiated the Adaptee object and the Adapter object, and used the Adapter object to call the request() method to adapt to specific requests of the Adaptee class. Running the above code will output "Adaptee specific_request", which indicates that the Adapter pattern successfully adapts the interface of the Adaptee class to the Target interface.

The Strategy pattern is a behavior design pattern that allows Selection algorithm at runtime. Python is an object-oriented programming language, which is very suitable for implementing Strategy pattern. The following is a simple example code that shows how to use Python to implement the Strategy pattern. ```python #Define Policy Interface class Strategy: def execute(self, num1, num2): pass #Define specific policy classes class AddStrategy(Strategy): def execute(self, num1, num2): return num1 + num2 class SubtractStrategy(Strategy): def execute(self, num1, num2): return num1 - num2 class MultiplyStrategy(Strategy): def execute(self, num1, num2): return num1 * num2 #Define Context Class class Context: def __init__(self, strategy): self.strategy = strategy def execute_strategy(self, num1, num2): return self.strategy.execute(num1, num2) #Usage examples context = Context(AddStrategy()) result = context.execute_strategy(5, 3) print(f"Add result: {result}") context = Context(SubtractStrategy()) result = context.execute_strategy(5, 3) print(f"Subtract result: {result}") context = Context(MultiplyStrategy()) result = context.execute_strategy(5, 3) print(f"Multiply result: {result}") ``` In the above example, we first defined the policy interface 'Strategy' and defined an 'execute' method in the interface. Then, we created three specific policy classes, namely 'AddStrategy', 'SubtractStrategy', and 'MultiplyStrategy', which implement the 'execute' method to execute different algorithms. Next, we define the context class' Context ', which receives a policy object in its constructor and provides an' execute '_ The strategy 'method is used to execute specific algorithms. Finally, we created an instance of 'Context' and executed different algorithms by passing in different policy objects, outputting the results. In this way, we can choose different policies at run time and apply them to specific problems, realizing the flexibility of Strategy pattern.

Prototype pattern is an object Creational pattern, whose purpose is to create new objects by copying the prototype of existing objects, rather than creating new objects through instantiation and initialization operations. In Python, you can use the 'copy' function provided by the 'copy' module to implement the Prototype pattern` The 'copy' function can be used for shallow and deep copying of objects. The following is an example code of using Python to implement the Prototype pattern: ```python import copy class Prototype: def __init__(self): self.objects = {} def register_object(self, name, obj): self.objects[name] = obj def unregister_object(self, name): del self.objects[name] def clone(self, name, **kwargs): obj = copy.deepcopy(self.objects.get(name)) obj.__dict__.update(kwargs) return obj class Object: def __init__(self, name): self.name = name def __str__(self): return f"Object: {self.name}" #Create prototype objects prototype = Prototype() #Create initial object obj1 = Object("Object 1") prototype.register_object("obj1", obj1) #Copy Object obj2 = prototype.clone("obj1", name="Object 2") print(obj1) print(obj2) ``` Run the above code and the output result is: ``` Object: Object 1 Object: Object 2 ``` In the example code, the 'Prototype' class is the definition of the prototype object, and the 'Object' class is the definition of the object to be copied. When creating a prototype object, first register the initial object and then use the 'clone' method to copy the object. The 'update' method allows you to update the properties of cloned objects. It should be noted that the 'copy. dropcopy' method is used to create deep copies of objects, ensuring that each cloned object is independent rather than sharing the same object.

Facade pattern is a structural design pattern, which is used to encapsulate various components of a complex system to simplify its use. In Python, you can use classes and methods to implement Facade pattern. The following is an example code of using Python to implement Facade pattern: ```python #Appearance class class Facade: def __init__(self): self.subsystem1 = Subsystem1() self.subsystem2 = Subsystem2() self.subsystem3 = Subsystem3() #Appearance method, encapsulating calls to subsystems def operation(self): self.subsystem1.operation1() self.subsystem2.operation2() self.subsystem3.operation3() #Subsystem 1 class Subsystem1: def operation1(self): print("Subsystem1 operation") #Subsystem 2 class Subsystem2: def operation2(self): print("Subsystem2 operation") #Subsystem 3 class Subsystem3: def operation3(self): print("Subsystem3 operation") #Client code def main(): facade = Facade() facade.operation() if __name__ == '__main__': main() ``` In the above code, the Facade class acts as a facade class, encapsulating calls to subsystems. Each subsystem has its own operating methods, and the Facade class encapsulates these operating methods in a facade method. The client code only needs to call the skin method through the skin class, without directly interacting with the subsystem. When we run the above code, the following results will be output: ``` Subsystem1 operation Subsystem2 operation Subsystem3 operation ``` This indicates that the appearance class successfully encapsulated calls to the subsystem, simplifying the client code.

The Iterator pattern is a behavior design pattern that provides a way to sequentially access elements in a container object without exposing the internal representation of the object. In Python, the generator and Iterator protocol can be used to implement the Iterator pattern. The following is an example code of using Python to implement the Iterator pattern: ```python class MyIterator: def __init__(self, data): self.data = data self.index = 0 def __iter__(self): return self def __next__(self): if self.index >= len(self.data): raise StopIteration value = self.data[self.index] self.index += 1 return value #Use Iterator my_list = [1, 2, 3, 4, 5] my_iterator = MyIterator(my_list) for item in my_iterator: print(item) ``` In the example code, we define a 'MyIterator' class that takes a data list as input and implements the`__ Iter__` And`__ Next__` Method`__ Iter__` Method returns the Iterator itself, and`__ Next__` Method is used to return the next element in the container object. Next, we created a 'my'_ List 'list and pass it to the instantiated object' my 'of the' MyIterator 'class_ Iterator `. Then, we use the 'for' loop to traverse 'my'_ Iterator 'object, the Iterator will return' my_ List each element and print it out. In this example, we successfully implemented the Iterator pattern in Python, accessing each element of the container object sequentially through the Iterator object without exposing the internal representation of the container object.

Command pattern is a behavior design pattern, which aims to encapsulate requests as objects for parameterization according to different requests. By encapsulating a request as an object, users can use different requests to parameterize other objects (such as logging, Transaction processing processing, or queues), and support undoable operations. The following is a complete sample code for implementing Command pattern in Python: ```python #Command interface class Command: def execute(self): pass #Command Implementation Class class LightOnCommand(Command): def __init__(self, light): self.light = light def execute(self): self.light.on() class LightOffCommand(Command): def __init__(self, light): self.light = light def execute(self): self.light.off() #Receiver of command class Light: def on(self): Print ("The light is on") def off(self): Print ("The light is off") #Command caller class RemoteControl: def __init__(self): self.command = None def set_command(self, command): self.command = command def press_button(self): self.command.execute() #Test Code def main(): light = Light() light_on_command = LightOnCommand(light) light_off_command = LightOffCommand(light) remote_control = RemoteControl() remote_control.set_command(light_on_command) Remote_ Control. press_ Button() # Turn on the light remote_control.set_command(light_off_command) Remote_ Control. press_ Button() # Turn off the light if __name__ == "__main__": main() ``` In the above example, the 'Command' interface defines the 'execute' method. The specific command classes' LightOnCommand 'and' LightOffCommand 'implement the' Command 'interface and encapsulate requests as objects. `The Light class, as the receiver of commands, performs the corresponding operations in the 'execute' methods of 'LightOnCommand' and 'LightOffCommand'. `The RemoteControl class serves as a command caller, using 'set'_ The command 'method sets specific commands and uses' press'_ The button 'method is used to execute commands. In this way, users can create different command implementation classes according to their needs and execute the corresponding commands by calling the 'RemoteControl' method. This achieves the goal of encapsulating, parameterizing, and revoking requests.

State mode is a behavior design pattern that allows objects to change their behavior when their internal state changes. In Python, implementing state patterns can simulate changes in state by using classes and polymorphism. The following is a complete sample code for implementing state patterns using Python. ```python class State: def write_name(self, name): pass class StateLowerCase(State): def write_name(self, name): print(name.lower()) class StateUpperCase(State): def write_name(self, name): print(name.upper()) class StateDefault(State): def write_name(self, name): print(name) class Context: def __init__(self): self.state = StateDefault() def set_state(self, state): self.state = state def write_name(self, name): self.state.write_name(name) #Usage examples context = Context() Context. write_ Name ("John") # Output: John context.set_state(StateLowerCase()) Context. write_ Name ("John") # Output: John context.set_state(StateUpperCase()) Context. write_ Name ("John") # Output: JOHN ``` In the above code, 'State' is an abstract class that represents the base class of the state` StateLowerCase ',' StateUpperCase ', and' StateDefault 'are specific state subclasses that implement' write 'respectively_ The name 'method changes the case of the input name` Write_ The name 'method implements different behaviors based on the current state. `The Context 'class is a context class that contains a state object and calls its methods to execute the corresponding behavior` Set_ The 'state' method is used to set the current state, 'write'_ Name 'method calls' write' in the current state_ Name 'method. By using state patterns, we can easily implement the function of objects taking different behaviors in different states.