Does range in python3 return an iterator?
What does the range of Pyhton3 return? Many people will say without thinking that this is not simple. In Python 2, range() will return a list. In Python 3, range has been replaced by xrange, and an iterator (Iterator) is returned.
Congratulations, you got the wrong answer.
range() returns an Iterable, not an Iterator.
a Python 3.6.3 (default, Nov 3 2017, 14:41:25) Type 'copyright', 'credits' or 'license' for more information IPython 6.2.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: a = range(10) In [2]: a Out[2]: range(0, 10) In [3]: import collections In [4]: isinstance(a, collections.Iterable) Out[4]: True In [5]: isinstance(a, collections.Iterator) Out[5]: False
The principle is very simple, let’s briefly talk about it first Iterable and Iterator, don't try to compare the two, because they are fundamentally different concepts. The literal meaning of both is very clear: Iterable is an iterable object, and calling iter(Iterable) on it will get an iterator; while Iterator is an iterator, and calling next(Iterator) on it will get the next element. .
Python advocates protocols, which to put it bluntly are duck types. If you implement __iter__(), (that is, you can get an Iterator by calling iter()), then you are an Iterable; if you implement __next__() and __iter__(), you are an Iterator.
Wait a minute, doesn’t Iterator just call next() to get the next element? Why does Iterator also need to implement Iterable's __iter__() method? This is not pure!
Why does Python's Iterator implement __iter__() (the usual implementation is return self). The official documentation says it quite clearly.
Iterators are required to have an __iter__() method that returns the iterator object itself so every iterator is also iterable and may be used in most places where other iterables are accepted.
A simple translation means that Iterator also requires the implementation of __iter__(), because the parameter received in many places is an Iterable. If all Iterators are Iterable, then these Iterable places can be used without any obstacles. Use Iterator. For example, let’s take a for loop. Regarding the for loop, I found this description in Python’s wiki (which is quite old):
Basically, any object with an iterable method can be used in a for loop. Even strings, despite not having an iterable method – but we’ll not get on to that here.
That is, the for loop gets an Iterable Iterator, and then uses this Iterator to perform Iterate. If Iterator implements the __iter__() method, then the for loop can iterate over Iterator without any hindrance, Neat! Imagine that Python's generator is also an Iterator. If the for loop cannot support iteration of Iterator, life would be worse than death.
So there is such an "excessive" requirement for Iterator. We can think that all Iterators are Iterable. So back to the original question, why does range() return an Iterable instead of an Iterator?
Considering that we usually use range(), we think it is a container that represents a range. You can use this container to initialize other containers without any problems.
>>> numbers = range(3) >>> tuple(numbers) (0, 1, 2) >>> tuple(numbers) (0, 1, 2)
If range() returns an iterator, then the above seemingly normal code is in trouble:
>>> numbers = iter(range(3)) >>> tuple(numbers) (0, 1, 2) >>> tuple(numbers) ()
Summary
Iterator is stateful and can only be traversed once. It is "consumption type" and cannot be "consumed twice". Iterable is stateless (not too rigorous here, let alone the Iterable of Iterator). Every time iter() is called on Iterable, a new iterator will be obtained.
The above is the detailed content of Does range in python3 return an iterator?. For more information, please follow other related articles on the PHP Chinese website!

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

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

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

Hot Topics



PHP and Python have their own advantages and disadvantages, and the choice depends on project needs and personal preferences. 1.PHP is suitable for rapid development and maintenance of large-scale web applications. 2. Python dominates the field of data science and machine learning.

Enable PyTorch GPU acceleration on CentOS system requires the installation of CUDA, cuDNN and GPU versions of PyTorch. The following steps will guide you through the process: CUDA and cuDNN installation determine CUDA version compatibility: Use the nvidia-smi command to view the CUDA version supported by your NVIDIA graphics card. For example, your MX450 graphics card may support CUDA11.1 or higher. Download and install CUDAToolkit: Visit the official website of NVIDIACUDAToolkit and download and install the corresponding version according to the highest CUDA version supported by your graphics card. Install cuDNN library:

Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.

Python and JavaScript have their own advantages and disadvantages in terms of community, libraries and resources. 1) The Python community is friendly and suitable for beginners, but the front-end development resources are not as rich as JavaScript. 2) Python is powerful in data science and machine learning libraries, while JavaScript is better in front-end development libraries and frameworks. 3) Both have rich learning resources, but Python is suitable for starting with official documents, while JavaScript is better with MDNWebDocs. The choice should be based on project needs and personal interests.

MinIO Object Storage: High-performance deployment under CentOS system MinIO is a high-performance, distributed object storage system developed based on the Go language, compatible with AmazonS3. It supports a variety of client languages, including Java, Python, JavaScript, and Go. This article will briefly introduce the installation and compatibility of MinIO on CentOS systems. CentOS version compatibility MinIO has been verified on multiple CentOS versions, including but not limited to: CentOS7.9: Provides a complete installation guide covering cluster configuration, environment preparation, configuration file settings, disk partitioning, and MinI

PyTorch distributed training on CentOS system requires the following steps: PyTorch installation: The premise is that Python and pip are installed in CentOS system. Depending on your CUDA version, get the appropriate installation command from the PyTorch official website. For CPU-only training, you can use the following command: pipinstalltorchtorchvisiontorchaudio If you need GPU support, make sure that the corresponding version of CUDA and cuDNN are installed and use the corresponding PyTorch version for installation. Distributed environment configuration: Distributed training usually requires multiple machines or single-machine multiple GPUs. Place

When installing PyTorch on CentOS system, you need to carefully select the appropriate version and consider the following key factors: 1. System environment compatibility: Operating system: It is recommended to use CentOS7 or higher. CUDA and cuDNN:PyTorch version and CUDA version are closely related. For example, PyTorch1.9.0 requires CUDA11.1, while PyTorch2.0.1 requires CUDA11.3. The cuDNN version must also match the CUDA version. Before selecting the PyTorch version, be sure to confirm that compatible CUDA and cuDNN versions have been installed. Python version: PyTorch official branch

CentOS Installing Nginx requires following the following steps: Installing dependencies such as development tools, pcre-devel, and openssl-devel. Download the Nginx source code package, unzip it and compile and install it, and specify the installation path as /usr/local/nginx. Create Nginx users and user groups and set permissions. Modify the configuration file nginx.conf, and configure the listening port and domain name/IP address. Start the Nginx service. Common errors need to be paid attention to, such as dependency issues, port conflicts, and configuration file errors. Performance optimization needs to be adjusted according to the specific situation, such as turning on cache and adjusting the number of worker processes.
