python概述_python基本概述-优快云博客

python概述

Python has many unique features that help make it what it is. Some of these features include:

Python具有许多独特的功能，可帮助使其成为现实。其中一些功能包括：

Python is Interpreted — Python compiles at runtime.

解释了Python-Python在运行时进行编译。

Python’s REPL — Interactive Python terminal

Python的REPL —交互式Python终端

Python is Multi-Paradigm — Multiple categories separated by features

Python是Multi-Paradigm —由功能分隔的多个类别

Python uses PIP package manager to install and manage packages in Python. (Uses PyPl as default source for packages and dependencies.

Python使用PIP软件包管理器在Python中安装和管理软件包。 (使用PyPl作为包和依赖项的默认来源。

Python supports OOP, structured programing, aspects of functional programing, as well as AOP (Aspect Orientated Programming). With extensions Python supports logical programming and design by contract.

Python支持OOP，结构化编程，功能编程的各个方面以及AOP(面向方面的编程)。通过扩展，Python支持按合同进行逻辑编程和设计。

Python uses dynamic typing, which means there is more overhead.

Python使用动态类型，这意味着更多的开销。

Python uses reference counting and cycle detecting garbage collecting for memory management.

Python使用引用计数和循环检测垃圾回收来进行内存管理。

Python focuses on clean and readable code.

Python专注于清晰易读的代码。

Python can be downloaded from https://python.org.

可以从https://python.org下载Python。

As always read the docs 👉 https://docs.python.org/3/.

与往常一样阅读docs👉https : //docs.python.org/3/。

Some of the major reasons that I personally love Python include the fact that it is just so easy to jump into as a beginner for a wide variety of projects. Python is a General-Purpose Language. That means that it does well at many different programming goals and systems. You can code the backend of a website, a business tool, do Explorative Data Analysis, create complex software, large calculations, or even machine learning / DEEP learning tasks. Also, for exploring data, you can use the REPL (Read Evaluate Print Loop) to bounce ideas off, figure out data structures, inspect data, test logic, and debug programming errors. Also, with the REPL you can dive into learning Python and seeing how variables and functions interact and behave. Another powerful tool in the Python toolbox is the Jupyter Notebooks, https://jupyter.org/. Jupyter Notebooks allow you to interact with Python inputs and receive clear outputs in a notebook like structure. The Jupyter Notebooks are used for an incredible range of purposes but are commonly used for data science and EDA (Exploratory Data Analysis). I love to use Python in this fashion myself. Just being able to see the process step by step and the ability to analyze different outputs is so important sometimes. One of the best implementations of this type of thinking is Google’s Colab Notebooks. https://colab.research.google.com. I use these notebooks all the time and they prove to be an incredible tool for an insurmountable number of tasks. In a way, the Colab Notebooks connect the power of the Jupyter Notebooks with the mobility of the Google Cloud.

我个人喜欢Python的一些主要原因包括以下事实：作为各种项目的初学者，它是如此容易。 Python是一种通用语言。这意味着它在许多不同的编程目标和系统上都表现出色。您可以对网站的后端，商业工具进行编码，进行探索性数据分析，创建复杂的软件，进行大量计算甚至是机器学习/ DEEP学习任务。此外，对于探索数据，您可以使用REPL(读取评估打印循环)来跳出思路，弄清楚数据结构，检查数据，测试逻辑和调试编程错误。此外，借助REPL，您可以深入学习Python，并了解变量和函数如何相互作用和行为。 Python工具箱中的另一个强大工具是Jupyter Notebooks， https： //jupyter.org/。 Jupyter Notebook可让您与Python输入进行交互，并在类似笔记本的结构中接收清晰的输出。 Jupyter笔记本的用途广泛，但通常用于数据科学和EDA(探索性数据分析)。我喜欢自己以这种方式使用Python。有时能够逐步了解流程以及分析不同输出的能力非常重要。此类思维的最佳实现之一是Google的Colab笔记本。 https://colab.research.google.com 。我一直在使用这些笔记本，事实证明它们是完成许多任务的不可思议的工具。在某种程度上，Colab笔记本将Jupyter笔记本的功能与Google Cloud的移动性联系在一起。

Python also follows Modular Package Importing, which means that the language is modular in design. I find this feature of Python to be especially useful and fun. Instead of having to code out classes, methods, and functions yourself you can effortlessly import packages of pre-written code. Close to every language has some level of grouping and encapsulation, so I am satisfied that Python choose to take this paradigm seriously. Partially due to Python being Open Source, it has a thriving community. With one of the most active fan bases of any programming language, Python boasts a sizable number of packages, large volumes of discussion on the internet, and an almost unmatchable amount of resources. There is no doubt that Python is a behemoth of a language.

Python还遵循模块化包导入，这意味着该语言在设计上是模块化的。我发现Python的这一功能特别有用且有趣。不必自己编写类，方法和函数，您可以轻松地导入预编写的代码包。几乎每种语言都有一定程度的分组和封装，因此我对Python选择认真对待这种范例感到满意。部分由于Python是开源的，它拥有一个繁荣的社区。 Python是所有编程语言中最活跃的粉丝基础之一，它拥有大量的程序包，互联网上的大量讨论以及几乎无与伦比的资源。毫无疑问，Python是语言的庞然大物。

For an effective way to get the most out of Python for Data Science you can download a Distribution of Python and R named Anaconda. This distribution is geared towards scientific computing. Anaconda is an entire article alone though. If you are interested check out the homepage here, https://www.anaconda.com/. The individual edition is open source. It comes with many unique and robust tools.

为了充分利用Python进行数据科学，您可以下载名为Anaconda的Python和R发行版。此分布适用于科学计算。 Anaconda仅是整篇文章。如果您有兴趣，请在此处查看主页https://www.anaconda.com/ 。单个版本是开源的。它带有许多独特而强大的工具。

As for IDEs there is, of course, a plethora of choices. Out of the sea of software my favorites are as follows:

对于IDE，当然有很多选择。在软件的海洋中，我的最爱如下：

PyCharm — https://www.jetbrains.com/pycharm/ — I cannot tell you how many times I use this IDE for my routine Python work. It is just the perfect setup for me. Not only the ease of having everything ready for Python use, but also, a full set of basic and advanced toolsets and properties that set this software in a different league, as far as, Python goes.
PyCharm — https://www.jetbrains.com/pycharm/ —我无法告诉您在此例行Python工作中使用此IDE的次数。这对我来说是完美的设置。就Python而言，不仅使一切准备就绪可轻松使用，而且还提供了全套基本和高级工具集和属性，使该软件处于另一个联盟。
Visual Studio Code — https://code.visualstudio.com/ — Not only do you have the clean, sleek, nice feel of the Microsoft Visual Studio product, but you get a ton of useful plugins, tools, and extensions. In my experience, VS Code handles Python beautifully.
Visual Studio代码— https://code.visualstudio.com/ —不仅具有Microsoft Visual Studio产品的简洁，时尚，漂亮的感觉，而且还获得了大量有用的插件，工具和扩展。以我的经验，VS Code可以很好地处理Python。
Visual Studio — https://visualstudio.microsoft.com/? — If a fully featured flagship IDE is something you are looking for then I recommend Visual Studio. Visual Studio comes in Community, Professional, and Enterprise editions. This is good for serious or in-depth projects.
Visual Studio — https://visualstudio.microsoft.com/？ —如果您正在寻找功能齐全的旗舰IDE，那么我建议使用Visual Studio。 Visual Studio具有社区版，专业版和企业版。这对于认真或深入的项目来说是好的。
Atom — https://atom.io/ — Sometimes an incredibly hackable IDE is suitable for the task and if so then GitHub’s Atom IDE is an excellent choice.
Atom — https://atom.io/ —有时，令人难以置信的可破解IDE适合该任务，如果是，那么GitHub的Atom IDE是一个不错的选择。

Python is increasing in popularity, functionality, and efficiency every day. It is never too late to jump on board with this language. With how truly mighty Python can be, it is no wonder that people all around the globe use it every day to solve simple to major involved issues. And with truly massive resources and packages for Machine Learning, DEEP Learning and AI, Python doesn’t look like it is going anywhere anytime soon. Some of them include:

Python的普及度，功能和效率每天都在增加。跳入这种语言永远不会太晚。 Python到底有多强大，难怪世界各地的人们每天都会使用它来解决从简单到重大的问题。有了用于机器学习，DEEP学习和AI的真正庞大的资源和程序包，Python看起来不会很快出现在任何地方。其中一些包括：

Tensor Flow
张量流
Py Tourch
Py Tourch
SciKit Learn
SciKit学习
Keras
凯拉斯
Theano
茶野

If you are looking to get into the Data Science aspects of Python, then https://kaggle.com is the place for you to dip your toes in. Not only do they offer free courses in Python, but they have a large community and plenty of fun challenges and competitions. Not to mention that Kaggle gives you a research environment and datasets within their platform. Also, they can be a good place to hire or get hired in the Data Science field. I really do like how Kaggle put together their system. It feels exceptionally clean gives you the important knowledge you need in a fun interactive way.

如果您想了解Python的数据科学方面的知识，那么https://kaggle.com就是您的理想之地。他们不仅提供免费的Python课程，而且社区很大，并且很多有趣的挑战和竞赛。更不用说Kaggle在其平台内为您提供了研究环境和数据集。而且，它们可以成为数据科学领域中雇用或被雇用的好地方。我真的很喜欢Kaggle如何整合他们的系统。感觉异常干净，以有趣的交互方式为您提供了所需的重要知识。

Another good learning resource if you are looking for more in-depth training is https://datacamp.com. They offer some free courses and some paid. They are incredibly good for diving into real problems and learning with incredible minds at the same time. I have learned to appreciate the time they put into making everything simple, sleek, and enjoyable. The relaxing UI and professional videos on top of the overall feel of the platform make me want to come back and learn more all the time. They also, have a huge community with lots to do within the platform. I recommend checking them out to anyone interested in Python for Data Science or R for Data Science.

如果您正在寻找更深入的培训，另一个很好的学习资源是https://datacamp.com 。他们提供一些免费课程和一些付费课程。他们非常擅长深入实际问题并同时以难以置信的头脑进行学习。我学会了欣赏他们投入的时间来使一切变得简单，时尚和愉快。令人放松的用户界面和专业视频，再加上平台的整体感觉，使我想一直回来学习更多的知识。他们还拥有一个庞大的社区，在平台内还有很多事情要做。我建议向对Python for Data Science或R for Data Science感兴趣的任何人检查一下。

Thank you for reading this article. If you saw any inaccurate or incorrect information, please leave it in the comments below. I plan to release more articles in the future, so keep an eye out. Happy programming!

感谢您阅读本文。如果您发现任何不正确或不正确的信息，请在下面的评论中保留。我计划将来发布更多文章，所以请注意。编程愉快！