


Foreword
Before talking about the difference between the two operators is and ==, you must first know the three basic elements contained in objects in Python, namely: id (identity), python type() (data type) and value (value). Both is and == are used to compare and judge objects, but the contents of object comparison and judgment are different. Let’s take a look at the specific differences.
There are two methods to compare whether two objects are equal in Python. Simply put, their differences are as follows:
is is to compare whether two references point to the same object (reference comparison).
== is to compare whether two objects are equal.
>>> a = [1, 2, 3] >>> b = a >>> b is a # a的引用复制给b,他们在内存中其实是指向了用一个对象 True >>> b == a # 当然,他们的值也是相等的 True >>> b = a[:] # b通过a切片获得a的部分,这里的切片操作重新分配了对象, >>> b is a # 所以指向的不是同一个对象了 False >>> b == a # 但他们的值还是相等的 True
Implementation principle
is comparing whether the two are the same object, so what is compared is the memory address (whether the id is the same).
== is a value comparison. Immutable objects, such as int, str, will directly compare values. For objects known to Python, their __eq__ function is called for comparison. (In fact, known objects should also be compared through the built-in __eq__ function). For custom objects, if the __eq__ function is implemented, it will be used for comparison. If it is not implemented, the effect is the same as ==.
Object caching mechanism
Python will cache relatively small objects. The next time a relatively small object is used, it will search in the cache area. If it is found, it will not open up new memory, but continue to cache the small object. The address of the object is assigned the new value. Example:
>>> c = 1 >>> d = 1 >>> print(c is d) True >>> 1000 is 10**3 False >>> 1000 == 10**3 True
The assignment is calculated and does not use the buffer area. This can be seen from the first code example.
For strings, you can force the use of the buffer by using the intern function.

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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