Iterator protocol and traversal methods in Python programming
Preface
The introduction of the previous part should make it easy for us to understand and master iterable objects and iterators (Iterable & Iterator) in Python. In this content, we will further introduce the related content of iterators.
1) __iter__(): This method of
object returns the iterator object itself. This is required for containers and iterators to be used with for-in statements. You can also use the built-in iter() function, which essentially calls the __iter__() method behind the scenes.2)__next__():
Return the next item from the iterator. If there are no other items, a StopIteration exception is thrown. You can also use the built-in next() function to read the next item, which is essentially calling the __next__() method. As we said before, lists, tuples, dictionaries and sets are all iterable types. In other words, they are types from which iterators can be obtained. Look at the example:A B CIn the sample code, we define a Iterate over tuples. Then call the iter() function on this iterable object. The iter() function returns an iterator, which we name tupIter. Then call the next() function multiple times. Each time the next() function is executed, it will automatically return the next item in the iterator. Look at the next sample code:
P yIn the above code listing, the __ iter__() method is called on the string object. String objects implement the iterator protocol, so strings are iterable objects that contain sequences of characters. Calling the __iter__() method directly returns an iterator. Then call the __next__() method through the returned iterator to output the elements in the iterator one by one. In a nutshell, as long as the object implements the iterator protocol, the object can be called iteratively according to the above two methods. Iterator traversalAs we saw in the previous introduction, we use the next() function (or __next__() method) to manually traverse the items of an iterator. When the next() function reaches the end of the iterator, there is no more data to return and you will get a StopIteration exception. Please see the example:
##10 ##20 30 ##Traceback (most recent call last): ##File ……, in StopIteration In the above code, the next() function is called four times, which is more than the number of items in the iterator. In the last call, a StopIteration exception was thrown - because the elements in the iterator have been iterated. Moreover, in order to ensure that exceptions may be thrown after the manual iteration is completed, exception handling must be performed by yourself, otherwise the subsequent execution will no longer be normal. What needs to be realized is: in most scenarios, we do not need to manually call the next method ourselves. The for loop in Python can automatically traverse any object that can return an iterator. In other words, a for loop can iterate over any iterable object in Python. Please see the example: Code List Snippet 04 In the above code, we use a for loop to traverse the list defined earlier. It is obvious that we did not use the next() function manually and did not get any StopIteration exception. This is the beauty of for loops in Python. It handles all of this for us behind the scenes. Of course, we can handle loop iteration ourselves this way. Now define our own version of the for loop. We will use a while loop and replicate the behavior of a for loop. Here we build everything needed for this implementation ourselves. As shown below: Code Listing Snippet-05 In the above listing, we have implemented our own version of a simulated for loop. An infinite while loop is used in the code: while True. A try-except block is set up inside the loop. In the try block, get the next element by calling the __next__() method on the iterator. If the call is successful, the element is printed. If an error of type StopIteration occurs, the exception is caught in the except block. What you do in the except block is very simple. We just break out of this loop, which means we've reached the end of the iterator. That’s it for this article, which mainly introduces the iterator protocol and iterator traversal. The text of the content is not long, and combined with the code can help you better understand and master these Python programming knowledge points. |
The above is the detailed content of Iterator protocol and traversal methods in Python programming. For more information, please follow other related articles on the PHP Chinese website!

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.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

SublimeText3 Linux new version
SublimeText3 Linux latest version

Dreamweaver Mac version
Visual web development tools

ZendStudio 13.5.1 Mac
Powerful PHP integrated development environment

SecLists
SecLists is the ultimate security tester's companion. It is a collection of various types of lists that are frequently used during security assessments, all in one place. SecLists helps make security testing more efficient and productive by conveniently providing all the lists a security tester might need. List types include usernames, passwords, URLs, fuzzing payloads, sensitive data patterns, web shells, and more. The tester can simply pull this repository onto a new test machine and he will have access to every type of list he needs.

SublimeText3 Mac version
God-level code editing software (SublimeText3)