Как в visual studio писать на python

Work with Python in Visual Studio on Windows

Applies to: Visual Studio Visual Studio for Mac Visual Studio Code

Python is a popular programming language that is reliable, flexible, easy to learn, free to use on all operating systems, and supported by both a strong developer community and many free libraries. Python supports all manners of development, including web applications, web services, desktop apps, scripting, and scientific computing. Scientists, casual developers, professional developers, and many universities alike use Python for programming. You can learn more about the language on python.org and Python for Beginners.

Visual Studio is a powerful Python IDE on Windows. Visual Studio provides open-source support for the Python language through the Python Development and Data Science workloads (Visual Studio 2017 and later) and the free Python Tools for Visual Studio extension (Visual Studio 2015 and earlier). Tour the Visual Studio IDE to familiarize yourself with the IDE features that you’ll use to write and edit Python code.

Visual Studio doesn’t support Python on Mac now. But it’s available on Mac and Linux through Visual Studio Code. (see questions and answers).

• Follow the installation instructions to set up the Python workload.
• Familiarize yourself with the Python capabilities of Visual Studio through the sections in this article.
• Go through one or more of the Quickstarts to create a project. If you’re unsure, start with Quickstart: Open and run Python code in a folder or Create a web app with Flask.
• Follow the Work with Python in Visual Studio tutorial for a full end-to-end experience.

Support for multiple interpreters

Visual Studio’s Python Environments window (shown below in a wide, expanded view) gives you a single place to manage all of your global Python environments, conda environments, and virtual environments. Visual Studio automatically detects installations of Python in standard locations, and allows you to configure custom installations. With each environment, you can easily manage packages, open an interactive window for that environment, and access environment folders.

Use the Open interactive window command to run Python interactively within the context of Visual Studio. Use the Open in PowerShell command to open a separate command window in the folder of the selected environment. From that command window you can run any python script.

For more information:

• Manage Python environments
• Python Environments reference

Rich editing, IntelliSense, and code comprehension

Visual Studio provides a first-class Python editor, including syntax coloring, autocomplete across all your code and libraries, code formatting, signature help, refactoring, linting, and type hints. Visual Studio also provides unique features like class view, Go to Definition, Find All References, and code snippets. Direct integration with the Interactive window helps you quickly develop Python code that’s already saved in a file.

For more information:

• Docs: Edit Python code
• Docs: Format code
• Docs: Refactor code
• Docs: Use a linter
• General Visual Studio feature docs: Features of the code editor

Interactive window

For every Python environment known to Visual Studio, you can easily open the same interactive (REPL) environment for a Python interpreter directly within Visual Studio, rather than using a separate command prompt. You can easily switch between environments as well. (To open a separate command prompt, select your desired environment in the Python Environments window, then select the Open in PowerShell command as explained earlier under Support for multiple interpreters.)

Visual Studio also provides tight integration between the Python code editor and the Interactive window. The Ctrl+Enter keyboard shortcut conveniently sends the current line of code (or code block) in the editor to the Interactive window, then moves to the next line (or block). Ctrl+Enter lets you easily step through code without having to run the debugger. You can also send selected code to the Interactive window with the same keystroke, and easily paste code from the Interactive window into the editor. Together, these capabilities allow you to work out details for a segment of code in the Interactive window and easily save the results in a file in the editor.

Visual Studio also supports IPython/Jupyter in the REPL, including inline plots, .NET, and Windows Presentation Foundation (WPF).

For more information:

• Python Interactive window
• IPython in Visual Studio

Project system, and project and item templates

Visual Studio 2019 supports opening a folder containing Python code and running that code without creating Visual Studio project and solution files. For more information, see Quickstart: Open and run Python code in a folder. There are, however, benefits to using a project file, as explained in this section.

Visual Studio 2022 supports opening a folder containing Python code and running that code without creating Visual Studio project and solution files. For more information, see Quickstart: Open and run Python code in a folder. There are, however, benefits to using a project file, as explained in this section.

Visual Studio helps you manage the complexity of a project as it grows over time. A Visual Studio project is much more than a folder structure: it includes an understanding of how different files are used and how they relate to each other. Visual Studio helps you distinguish app code, test code, web pages, JavaScript, build scripts, and so on, which then enable file-appropriate features. A Visual Studio solution, moreover, helps you manage multiple related projects, such as a Python project and a C++ extension project.

Project and item templates automate the process of setting up different types of projects and files, saving you valuable time and relieving you from managing intricate and error-prone details. Visual Studio provides templates for web, Azure, data science, console, and other types of projects, along with templates for files like Python classes, unit tests, Azure web configuration, HTML, and even Django apps.

For more information:

• Docs: Manage Python projects
• Docs: Item templates reference
• Docs: Python project templates
• Docs: Work with C++ and Python
• General Visual Studio feature docs: Project and item templates
• General Visual Studio feature docs: Solutions and projects in Visual Studio

Full-featured debugging

One of Visual Studio’s strengths is its powerful debugger. For Python in particular, Visual Studio includes Python/C++ mixed-mode debugging, remote debugging on Linux, debugging within the Interactive window, and debugging Python unit tests.

In Visual Studio 2019, you can run and debug code without having a Visual Studio project file. See Quickstart: Open and run Python code in a folder-2019 for an example.

In Visual Studio 2022, you can run and debug code without having a Visual Studio project file. See Quickstart: Open and run Python code in a folder-2022 for an example.

For more information:

• Docs: Debug Python
• Docs: Python/C++ mixed-mode debugging
• Docs: Remote debugging on Linux
• General Visual Studio feature docs: Feature tour of the Visual Studio Debugger

Profiling tools with comprehensive reporting

Profiling explores how time is being spent within your application. Visual Studio supports profiling with CPython-based interpreters and includes the ability to compare performance between different profiling runs.

For more information:

• Docs: Python profiling tools
• General Visual Studio feature docs: Profiling Feature Tour. (Not all Visual Studio profiling features are available for Python).

Unit testing tools

Discover, run, and manage tests in Visual Studio Test Explorer, and easily debug unit tests.

For more information:

• Docs: Unit testing tools for Python
• General Visual Studio feature docs: Unit test your code.

Azure SDK for Python

The Azure libraries for Python simplify consuming Azure services from Windows, macOS X, and Linux apps. You can use them to create and manage Azure resources, and to connect to Azure services.

Questions and answers

Q. Is Python support available with Visual Studio for Mac?

A. Not at this time, but you can up vote the request on Developer Community. The Visual Studio for Mac documentation identifies the current types of development that it does support. In the meantime, Visual Studio Code on Windows, Mac, and Linux works well with Python through available extensions.

Q. What can I use to build UI with Python?

A. The main offering in this area is the Qt Project, with bindings for Python known as PySide (the official binding) (also see PySide downloads) and PyQt. Python support in Visual Studio doesn’t include any specific tools for UI development.

Q. Can a Python project produce a stand-alone executable?

A. Python is generally an interpreted language, with which code is run on demand in a suitable Python-capable environment such as Visual Studio and web servers. Visual Studio itself doesn’t now provide the means to create a stand-alone executable, which essentially means a program with an embedded Python interpreter. However, the Python community supplied different means to create executables as described on StackOverflow. CPython also supports being embedded within a native application, as described on the blog post, Using CPython’s embeddable zip file.

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Getting Started with Python in VS Code

In this tutorial, you will learn how to use Python 3 in Visual Studio Code to create, run, and debug a Python «Roll a dice» application, work with virtual environments, use packages, and more! By using the Python extension, you turn VS Code into a great, lightweight Python editor.

If you are new to programming, check out the Visual Studio Code for Education — Introduction to Python course. This course offers a comprehensive introduction to Python, featuring structured modules in a ready-to-code browser-based development environment.

To gain a deeper understanding of the Python language, you can explore any of the programming tutorials listed on python.org within the context of VS Code.

For a Data Science focused tutorial with Python, check out our Data Science section.

Prerequisites

To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:

• Python 3
• VS Code
• VS Code Python extension (For additional details on installing extensions, see Extension Marketplace)

Install a Python interpreter

Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.

Windows

Install Python from python.org. Use the Download Python button that appears first on the page to download the latest version.

Note: If you don’t have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of supported Python versions.

For additional information about using Python on Windows, see Using Python on Windows at Python.org

macOS

The system install of Python on macOS is not supported. Instead, a package management system like Homebrew is recommended. To install Python using Homebrew on macOS use brew install python3 at the Terminal prompt.

Note: On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

Linux

The built-in Python 3 installation on Linux works well, but to install other Python packages you must install pip with get-pip.py.

Other options

• Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.
• Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you’ll also want to install the WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.

Note: To verify that you’ve installed Python successfully on your machine, run one of the following commands (depending on your operating system):

Linux/macOS: open a Terminal Window and type the following command:

python3 --version 

Windows: open a command prompt and run the following command:

py -3 --version 

If the installation was successful, the output window should show the version of Python that you installed. Alternatively, you can use the py -0 command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

Start VS Code in a workspace folder

By starting VS Code in a folder, that folder becomes your «workspace».

Using a command prompt or terminal, create an empty folder called «hello», navigate into it, and open VS Code ( code ) in that folder ( . ) by entering the following commands:

mkdir hello cd hello code . 

Note: If you’re using an Anaconda distribution, be sure to use an Anaconda command prompt.

Alternately, you can create a folder through the operating system UI, then use VS Code’s File > Open Folder to open the project folder.

Create a virtual environment

A best practice among Python developers is to use a project-specific virtual environment . Once you activate that environment, any packages you then install are isolated from other environments, including the global interpreter environment, reducing many complications that can arise from conflicting package versions. You can create non-global environments in VS Code using Venv or Anaconda with Python: Create Environment.

Open the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ), start typing the Python: Create Environment command to search, and then select the command.

The command presents a list of environment types, Venv or Conda. For this example, select Venv.

The command then presents a list of interpreters that can be used for your project. Select the interpreter you installed at the beginning of the tutorial.

After selecting the interpreter, a notification will show the progress of the environment creation and the environment folder ( /.venv ) will appear in your workspace.

Ensure your new environment is selected by using the Python: Select Interpreter command from the Command Palette.

Note: For additional information about virtual environments, or if you run into an error in the environment creation process, see Environments.

Create a Python source code file

From the File Explorer toolbar, select the New File button on the hello folder:

Name the file hello.py , and VS Code will automatically open it in the editor:

By using the .py file extension, you tell VS Code to interpret this file as a Python program, so that it evaluates the contents with the Python extension and the selected interpreter.

Note: The File Explorer toolbar also allows you to create folders within your workspace to better organize your code. You can use the New folder button to quickly create a folder.

Now that you have a code file in your Workspace, enter the following source code in hello.py :

msg = "Roll a dice" print(msg) 

When you start typing print , notice how IntelliSense presents auto-completion options.

IntelliSense and auto-completions work for standard Python modules as well as other packages you’ve installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the msg variable contains a string, IntelliSense provides string methods when you type msg. :

Finally, save the file ( ⌘S (Windows, Linux Ctrl+S ) ). At this point, you’re ready to run your first Python file in VS Code.

For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.

Run Python code

Click the Run Python File in Terminal play button in the top-right side of the editor.

The button opens a terminal panel in which your Python interpreter is automatically activated, then runs python3 hello.py (macOS/Linux) or python hello.py (Windows):

There are three other ways you can run Python code within VS Code:

1. Right-click anywhere in the editor window and select Run > Python File in Terminal (which saves the file automatically):
2. Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.
3. From the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Congrats, you just ran your first Python code in Visual Studio Code!

Configure and run the debugger

Let’s now try debugging our Python program.

First, set a breakpoint on line 2 of hello.py by placing the cursor on the print call and pressing F9 . Alternately, click in the editor’s left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.

Next, to initialize the debugger, press F5 . Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.

Note: VS Code uses JSON files for all of its various configurations; launch.json is the standard name for a file containing debugging configurations.

Select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

Start the debugger by clicking on the down-arrow next to the run button on the editor, and selecting Debug Python File in Terminal.

The debugger will stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you will see now defined msg variable appears in the Local pane.

A debug toolbar appears along the top with the following commands from left to right: continue ( F5 ), step over ( F10 ), step into ( F11 ), step out ( ⇧F11 (Windows, Linux Shift+F11 ) ), restart ( ⇧⌘F5 (Windows, Linux Ctrl+Shift+F5 ) ), and stop ( ⇧F5 (Windows, Linux Shift+F5 ) ).

The Status Bar also changes color (orange in many themes) to indicate that you’re in debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run, along with the program output.

To continue running the program, select the continue command on the debug toolbar ( F5 ). The debugger runs the program to the end.

Tip Debugging information can also be seen by hovering over code, such as variables. In the case of msg , hovering over the variable will display the string Roll a dice! in a box above the variable.

You can also work with variables in the Debug Console (If you don’t see it, select Debug Console in the lower right area of VS Code, or select it from the . menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:

msg msg.capitalize() msg.split() 

Select the blue Continue button on the toolbar again (or press F5 ) to run the program to completion. «Roll a dice!» appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.

If you restart the debugger, the debugger again stops on the first breakpoint.

To stop running a program before it’s complete, use the red square stop button on the debug toolbar ( ⇧F5 (Windows, Linux Shift+F5 ) ), or use the Run > Stop debugging menu command.

For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.

Tip: Use Logpoints instead of print statements: Developers often litter source code with print statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn’t stop the program. For more information, see Logpoints in the main VS Code debugging article.

Install and use packages

Let’s build upon the previous example by using packages.

In Python, packages are how you obtain any number of useful code libraries, typically from PyPI, that provide additional functionality to your program. For this example, you use the numpy package to generate a random number.

Return to the Explorer view (the top-most icon on the left side, which shows files), open hello.py , and paste in the following source code:

import numpy as np msg = "Roll a dice" print(msg) print(np.random.randint(1,9)) 

Tip: If you enter the above code by hand, you may find that auto-completions change the names after the as keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter .

Next, run the file in the debugger using the «Python: Current file» configuration as described in the last section.

You should see the message, «ModuleNotFoundError: No module named ‘numpy'». This message indicates that the required package isn’t available in your interpreter. If you’re using an Anaconda distribution or have previously installed the numpy package you may not see this message.

To install the numpy package, stop the debugger and use the Command Palette to run Terminal: Create New Terminal ( ⌃⇧` (Windows, Linux Ctrl+Shift+` ) ). This command opens a command prompt for your selected interpreter.

To install the required packages in your virtual environment, enter the following commands as appropriate for your operating system:

Install the packages

# Don't use with Anaconda distributions because they include matplotlib already. # macOS python3 -m pip install numpy # Windows (may require elevation) py -m pip install numpy # Linux (Debian) apt-get install python3-tk python3 -m pip install numpy 

Congrats on completing the Python tutorial! During the course of this tutorial, you learned how to create a Python project, create a virtual environment, run and debug your Python code, and install Python packages. Explore additional resources to learn how to get the most out of Python in Visual Studio Code!

Next steps

To learn how to build web apps with popular Python web frameworks, see the following tutorials:

• Use Django in Visual Studio Code
• Use Flask in Visual Studio Code
• Use FastAPI in Visual Studio Code

There is then much more to explore with Python in Visual Studio Code:

• Python profile template — Create a new profile with a curated set of extensions, settings, and snippets
• Editing code — Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
• Linting — Enable, configure, and apply a variety of Python linters.
• Debugging — Learn to debug Python both locally and remotely.
• Testing — Configure test environments and discover, run, and debug tests.
• Settings reference — Explore the full range of Python-related settings in VS Code.
• Deploy Python to Azure App Service
• Deploy Python to Container Apps

Руководство. Работа с Python в Visual Studio

Область применения:Visual Studio Visual Studio для Mac Visual Studio Code

Python — это популярный язык программирования, который отличается надежностью, гибкостью и простотой освоения. Его можно бесплатно использовать на любых операционных системах. Он поддерживается широким сообществом разработчиков. Кроме того, для него доступно множество бесплатных библиотек. Этот язык поддерживает все виды разработки, в том числе веб-приложения, веб-службы, классические приложения, создание сценариев и научные вычисления. Поэтому Python используется множеством университетов, ученых, разработчиков-любителей и профессиональных разработчиков.

Visual Studio обеспечивает первоклассную поддержку языка Python. В этом учебнике рассматриваются перечисленные ниже действия.

• Шаг 1. Создание проекта Python (эта статья).
• Шаг 2. Написание и выполнение кода для ознакомления с функцией IntelliSense в Visual Studio на практике.
• Шаг 3. Создание дополнительного кода в интерактивном окне REPL.
• Шаг 4. Выполнение готовой программы в отладчике Visual Studio
• Шаг 5. Установка пакетов и управление средами Python.
• Шаг 6. Работа с Git.

Необходимые компоненты

Visual Studio с установленной рабочей нагрузкой Python. Инструкции см. в разделе «Установка средств Python для Visual Studio».

Шаг 1. Создание проекта Python

С помощью проекта в Visual Studio производится управление всеми файлами, составляющими приложение. К файлам приложения можно отнести исходный код, ресурсы, конфигурации. Проект формализует и поддерживает связи между всеми файлами проекта. Проект также управляет внешними ресурсами, которые являются общими для нескольких проектов. Проект позволяет приложению легко расширяться и увеличивать свой размер. Использовать проекты гораздо проще, чем вручную контролировать связи с помощью специальных папок, скриптов, текстовых файлов и собственной памяти.

В этом учебнике вы начнете работу с простого проекта, содержащего один пустой файл кода.

1. В Visual Studio выберите Файл>Создать>Проект (CTRL+SHIFT+N), после чего откроется диалоговое окно Создание проекта. В нем можно просмотреть шаблоны для разных языков, после чего выбрать один из них для вашего проекта и указать, куда среда Visual Studio должна поместить файлы.
2. Чтобы просмотреть шаблоны Python, выберите Установленные>Python в области слева или выполните поиск по слову «Python». Поиск — это отличный способ найти шаблон, если вы не помните, где он находится в дереве языков. Поддержка Python в Visual Studio включает в себя несколько шаблонов проектов, включая веб-приложения на платформах Bottle, Flask и Django. Однако для целей данного пошагового руководства мы начнем с пустого проекта.
3. Выберите шаблон Приложение Python, укажите имя проекта и нажмите кнопку ОК.
4. Через несколько секунд в окне обозревателя решений Visual Studio (1) будет показана структура проекта. Файл кода по умолчанию откроется в редакторе (2). Кроме того, откроется окно Свойства (3), в котором приводятся дополнительные сведения для элемента, выбранного в обозревателе решений, включая его точное расположение на диске.
5. Потратьте несколько минут на знакомство с обозревателем решений, который служит для просмотра файлов и папок проекта. (1) Полужирным шрифтом выделен ваш проект, имя которого вы указали в окне Создание проекта. На диске этот проект представлен файлом .pyproj в папке проекта. (2) На верхнем уровне находится решение, имя которого по умолчанию совпадает с именем проекта. Решение, представленное на диске файлом SLN, является контейнером для одного или нескольких связанных проектов. Например, если вы создаете расширение C++ для приложения Python, этот проект C++ может входить в то же решение. Решение также может включать в себя проект веб-службы и проекты специальных тестовых программ. (3) В проекте можно увидеть файлы исходного кода. В нашем примере это один файл .py. При выборе файла его свойства приводятся в окне Свойства. Если дважды щелкнуть файл, он откроется в соответствующем средстве. (4) Кроме того, в проекте есть узел Окружения Python. Если развернуть его, можно увидеть доступные интерпретаторы Python. Развернув узел интерпретатора, вы увидите библиотеки, установленные в этой среде (5). Щелкните правой кнопкой мыши любой узел или элемент в обозревателе решений, чтобы открыть меню с применимыми командами. Например, команда «Переименовать » позволяет изменить имя любого узла или элемента, включая проект и решение.

1. В Visual Studio последовательно выберите Файл>Создать>Проект или нажмите комбинацию клавиш CTRL+SHIFT+N. Откроется экран Создание проекта, где можно искать и просматривать шаблоны на разных языках.
2. Чтобы просмотреть шаблоны Python, выполните поиск по слову python. Поиск — это отличный способ найти шаблон, если вы не помните, где он находится в дереве языков. Поддержка веб-сайтов Python в Visual Studio включает несколько шаблонов проектов, таких как веб-приложения в платформах Bottle, Flask и Django. При установке Python с помощью установщика Visual Studio проверка «Веб-поддержка Python» при необходимости, чтобы установить эти шаблоны. Для целей этого учебника мы начнем работу с пустого проекта.
3. Выберите шаблон приложения Python и нажмите кнопку «Далее«.
4. На экране Настроить новый проект укажите имя и расположение файла проекта, а затем нажмите кнопку Создать. Новый проект откроется в Visual Studio.
   • В окне Обозреватель решений Visual Studio будет показана структура проекта (1).
   • Файл кода по умолчанию откроется в редакторе (2).
   • Откроется окно Свойства, в котором приводятся дополнительные сведения для элемента, выбранного в Обозревателе решений, включая его точное расположение на диске (3).

Ознакомьтесь с Обозревателем решений, который можно использовать для просмотра файлов и папок проекта.

• На верхнем уровне находится решение, имя которого по умолчанию совпадает с именем проекта (1). Решение, представленное на диске файлом .sln, является контейнером для одного или нескольких связанных проектов. Например, если вы создаете расширение C++ для приложения Python, этот проект C++ может входить в то же решение. Решение также может включать в себя проект веб-службы и проекты специальных тестовых программ.
• Ваш проект, имя которого вы указали в диалоговом окне Создание проекта, будет выделен полужирным шрифтом (2). На диске проект представлен файлом PYPROJ в папке проекта.
• В проекте можно увидеть исходные файлы. В нашем примере это один файл PY ((3)). При выборе файла отображаются его свойства в окне «Свойства» (если окно свойствне отображается, щелкните ключ в баннере Обозреватель решений). Если дважды щелкнуть файл, он откроется в соответствующем средстве.
• Кроме того, в проекте есть узел Окружения Python ((4)). Разверните узел, чтобы отобразить доступные интерпретаторы Python.
• Развернув узел интерпретатора, вы увидите библиотеки, установленные в этой среде (5).

Щелкните правой кнопкой мыши любой узел или элемент в Обозревателе решений, чтобы открыть контекстное меню с применимыми командами. Например, с помощью команды Переименовать можно изменить имя любого узла или элемента, в том числе проекта и решения.

Следующий шаг

Дополнительные подробности

• Проекты Python в Visual Studio
• Сведения о языке Python на сайте python.org
• Python для начинающих (python.org)

Tutorial: Work with Python in Visual Studio

Applies to: Visual Studio Visual Studio for Mac Visual Studio Code

Python is a popular programming language that is reliable, flexible, easy to learn, and free to use on all operating systems. Python is supported by both a strong developer community and many, free libraries. The language supports all kinds of development, including web applications, web services, desktop apps, scripting, and scientific computing. Many universities, scientists, casual developers, and professional developers use Python.

Visual Studio provides first-class language support for Python. This tutorial guides you through the following steps:

• Step 1: Create a Python project (this article)
• Step 2: Write and run code to see Visual Studio IntelliSense at work
• Step 3: Create more code in the Interactive REPL window
• Step 4: Run the completed program in the Visual Studio debugger
• Step 5: Install packages and manage Python environments
• Step 6: Work with Git

Prerequisites

Visual Studio with the Python workload installed. For instructions, see Install Python tools for Visual Studio.

Step 1: Create a new Python project

A project is how Visual Studio manages all the files that come together to produce a single application. Application files include source code, resources, and configurations. A project formalizes and maintains the relationships among all the project’s files. The project also manages external resources that are shared between multiple projects. A project allows your application to effortlessly expand and grow. Using projects is much easier than manually managing relationships in unplanned folders, scripts, text files, and your memory.

This tutorial begins with a simple project containing a single, empty code file.

1. In Visual Studio, select File >New >Project (Ctrl+Shift+N), which brings up the New Project dialog. Here you browse templates across different languages, then select one for your project and specify where Visual Studio places files.
2. To view Python templates, select Installed >Python on the left, or search for «Python». Using search is a great way to find a template when you can’t remember its location in the languages tree. Python support in Visual Studio includes several project templates, including web applications using the Bottle, Flask, and Django frameworks. For the purposes of this walkthrough, however, let’s start with an empty project.
3. Select the Python Application template, specify a name for the project, and select OK.
4. After a few moments, Visual Studio shows the project structure in the Solution Explorer window (1). The default code file is open in the editor (2). The Properties window (3) also appears to show additional information for any item selected in Solution Explorer, including its exact location on disk.
5. Take a few moments to familiarize yourself with Solution Explorer, which is where you browse files and folders in your project. (1) Highlighted in bold is your project, using the name you gave in the New Project dialog. On disk, this project is represented by a .pyproj file in your project folder. (2) At the top level is a solution, which by default has the same name as your project. A solution, represented by a .sln file on disk, is a container for one or more related projects. For example, if you write a C++ extension for your Python application, that C++ project could be in the same solution. The solution might also contain a project for a web service, along with projects for dedicated test programs. (3) Under your project you see source files, in this case only a single .py file. Selecting a file displays its properties in the Properties window. Double-clicking a file opens it in whatever way is appropriate for that file. (4) Also under the project is the Python Environments node. When expanded, you see the Python interpreters that are available to you. Expand an interpreter node to see the libraries that are installed into that environment (5). Right-click any node or item in Solution Explorer to access a menu of applicable commands. For example, the Rename command lets you to change the name of any node or item, including the project and the solution.

1. In Visual Studio, select File >New >Project or press Ctrl+Shift+N. The Create a new project screen displays, where you can search and browse templates across different languages.
2. To view Python templates, search for python. Search is a great way to find a template when you can’t remember its location in the languages tree. Python web support in Visual Studio includes several project templates, such as web applications in the Bottle, Flask, and Django frameworks. When installing Python with the Visual Studio Installer, check «Python Web Support» under optional to install these templates. For this tutorial, start with an empty project.
3. Select the Python Application template, and select Next.
4. On the Configure your new project screen, specify a name and file location for the project, and then select Create. The new project opens in Visual Studio.
   • The Visual Studio Solution Explorer window shows the project structure (1).
   • The default code file opens in the editor (2).
   • The Properties window shows more information for the item selected in Solution Explorer, including its exact location on disk (3).

Familiarize yourself with Solution Explorer, where you can browse files and folders in your project.

• At the top level is the solution, which by default has the same name as your project (1). A solution, which is shown as a .sln file on disk, is a container for one or more related projects. For example, if you write a C++ extension for your Python application, that C++ project can be in the same solution. The solution could also contain a project for a web service, and projects for dedicated test programs.
• Your project, with the name you gave in the Create a new project dialog box, displays in bold (2). On disk, the project is a .pyproj file in your project folder.
• Under your project are source files, in this case only a single .py file (3). Selecting a file displays its properties in the Properties window (if you do not see the Properties window, click the wrench in the Solution Explorer banner). Double-clicking a file opens it in whatever way is appropriate for that file.
• Also under the project is the Python Environments node (4). Expand the node to show the available Python interpreters.
• Expand an interpreter node to see the libraries installed in that environment (5).

Right-click any node or item in Solution Explorer to show a context menu of applicable commands. For example, Rename lets you change the name of a node or item, including the project and the solution.

Next step

Go deeper

• Python projects in Visual Studio.
• Learn about the Python language on python.org
• Python for Beginners (python.org)

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