正常情况下,当我们定义了一个class,创建了一个class的实例后,我们可以给该实例绑定任何属性和方法,这就是动态语言的灵活性。先定义class:
>>> class Student(object): ... pass ...
然后,尝试给实例绑定一个属性:
>>> s = Student() >>> s.name = 'Michael' # 动态给实例绑定一个属性 >>> print s.name Michael
还可以尝试给实例绑定一个方法:
>>> def set_age(self, age): # 定义一个函数作为实例方法 ... self.age = age ... >>> from types import MethodType >>> s.set_age = MethodType(set_age, s, Student) # 给实例绑定一个方法 >>> s.set_age(25) # 调用实例方法 >>> s.age # 测试结果 25
但是,给一个实例绑定的方法,对另一个实例是不起作用的:
>>> s2 = Student() # 创建新的实例 >>> s2.set_age(25) # 尝试调用方法 Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'Student' object has no attribute 'set_age'
为了给所有实例都绑定方法,可以给class绑定方法:
>>> def set_score(self, score): ... self.score = score ... >>> Student.set_score = MethodType(set_score, None, Student)
给class绑定方法后,所有实例均可调用:
>>> s.set_score(100) >>> s.score 100 >>> s2.set_score(99) >>> s2.score 99
通常情况下,上面的set_score方法可以直接定义在class中,但动态绑定允许我们在程序运行的过程中动态给class加上功能,这在静态语言中很难实现。
使用__slots__
但是,如果我们想要限制class的属性怎么办?比如,只允许对Student实例添加name和age属性。
为了达到限制的目的,Python允许在定义class的时候,定义一个特殊的__slots__变量,来限制该class能添加的属性:
>>> class Student(object): ... __slots__ = ('name', 'age') # 用tuple定义允许绑定的属性名称 ...
然后,我们试试:
>>> s = Student() # 创建新的实例 >>> s.name = 'Michael' # 绑定属性'name' >>> s.age = 25 # 绑定属性'age' >>> s.score = 99 # 绑定属性'score' Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'Student' object has no attribute 'score'
由于'score'没有被放到__slots__中,所以不能绑定score属性,试图绑定score将得到AttributeError的错误。
使用__slots__要注意,__slots__定义的属性仅对当前类起作用,对继承的子类是不起作用的:
>>> class GraduateStudent(Student): ... pass ... >>> g = GraduateStudent() >>> g.score = 9999 Try
除非在子类中也定义__slots__,这样,子类允许定义的属性就是自身的__slots__加上父类的__slots__。

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.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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