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Getting Started with Python in VS Code
In this tutorial, you use Python 3 to create the simplest Python «Hello World» application in Visual Studio Code. By using the Python extension, you make VS Code into a great lightweight Python IDE (which you may find a productive alternative to PyCharm).
This tutorial introduces you to VS Code as a Python environment, primarily how to edit, run, and debug code through the following tasks:
This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on python.org within the context of VS Code for an introduction to the language.
If you have any problems, feel free to file an issue for this tutorial in the VS Code documentation repository.
Prerequisites
To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:
Install Visual Studio Code and the Python Extension
If you have not already done so, install VS Code.
Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and it’s published by Microsoft.
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. You can typically 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 Python 3.7, Python 3.8, and Python 3.9. Be aware that you might have compatibility issues with some packages using this method.
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, an installation through 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.
Verify the Python installation
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:
Windows: open a command prompt and run the following command:
If the installation was successful, the output window should show the version of Python that you installed.
Start VS Code in a project (workspace) folder
Note: If you’re using an Anaconda distribution, be sure to use an Anaconda command prompt.
Alternately, you can run VS Code through the operating system UI, then use File > Open Folder to open the project folder.
Select a Python interpreter
Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.
From within VS Code, select a Python 3 interpreter by opening the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):
The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don’t see the desired interpreter, see Configuring Python environments.
Selecting an interpreter sets which interpreter will be used by the Python extension for that workspace.
Note: If you select an interpreter without a workspace folder open, VS Code sets python.defaultInterpreterPath in User scope instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.
Create a Python Hello World source code file
From the File Explorer toolbar, select the New File button on the hello folder:
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 :
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. :
Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the msg variable and the print call, and save the file ( ⌘S (Windows, Linux Ctrl+S ) ).
For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.
Run Hello World
It’s simple to run hello.py with Python. Just 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:
Right-click anywhere in the editor window and select Run Python File in Terminal (which saves the file automatically):
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.
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.
Configure and run the debugger
Let’s now try debugging our simple Hello World program.
Note: VS Code uses JSON files for all of its various configurations; launch.json is the standard name for a file containing debugging configurations.
These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.
You can also 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.
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:
Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. «Hello World» 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 now run an example that’s a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the matplotlib and numpy packages to create a graphical plot as is commonly done with data science. (Note that matplotlib cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)
Next, try running the file in the debugger using the «Python: Current file» configuration as described in the last section.
Unless you’re using an Anaconda distribution or have previously installed the matplotlib package, you should see the message, «ModuleNotFoundError: No module named ‘matplotlib'». Such a message indicates that the required package isn’t available in your system.
To install the matplotlib package (which also installs numpy as a dependency), 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.
A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific virtual environment that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:
Note: For additional information about virtual environments, see Environments.
Create and activate the virtual environment
Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.
For Windows
If the activate command generates the message «Activate.ps1 is not digitally signed. You cannot run this script on the current system.», then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):
For macOS/Linux
Select your new environment by using the Python: Select Interpreter command from the Command Palette.
Install the packages
Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:
Once you are finished, type deactivate in the terminal window to deactivate the virtual environment.
For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.
Next steps
You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.
To learn more about the Python language, follow any of the programming tutorials listed on python.org within the context of VS Code.
To learn to build web apps with the Django and Flask frameworks, see the following tutorials:
There is then much more to explore with Python in Visual Studio Code:
Руководство. Работа с Python в Visual Studio
Python — это популярный язык программирования, который отличается надежностью, гибкостью и простотой освоения. Его можно бесплатно использовать на любых операционных системах, и он поддерживается широким сообществом разработчиков. Кроме того, для него доступно множество бесплатных библиотек. Python поддерживает все способы разработки, включая веб-приложения, веб-службы, классические приложения, скрипты и научные вычисления. Его используют многие университеты, ученые, профессиональные и непрофессиональные разработчики.
Visual Studio обеспечивает первоклассную поддержку языка Python. В этом учебнике рассматриваются перечисленные ниже действия.
предварительные требования
Вы также можете использовать более раннюю версию Visual Studio с установленным подключаемым модулем Инструменты Python для Visual Studio. См. руководство по установке поддержки Python в Visual Studio.
Шаг 1. Создание проекта Python
С помощью проекта в Visual Studio производится управление всеми файлами, составляющими приложение, включая исходный код, ресурсы, конфигурации и другие данные. Проект формализует и обеспечивает взаимосвязь между всеми файлами проекта, а также между ними и внешними ресурсами, которые используются несколькими проектами. Таким образом, благодаря проектам расширять и развивать приложение становится гораздо проще, чем когда вы контролируете взаимосвязи в произвольных папках, скриптах, текстовых файлах или даже у себя в голове.
В этом учебнике вы начнете работу с простого проекта, содержащего один пустой файл кода.
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.
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.
Обратите внимание на то, что поддержка Python в Visual Studio включает в себя ряд шаблонов проектов, включая веб-приложения на платформах Bottle, Flask и Django. Однако для целей данного пошагового руководства мы начнем с пустого проекта.
Выберите шаблон Приложение Python, укажите имя проекта и нажмите кнопку ОК.
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.
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, с помощью команды Переименовать можно изменить имя любого узла или элемента, включая проект и решение.
Python in Visual Studio Code
Working with Python in Visual Studio Code, using the Microsoft Python extension, is simple, fun, and productive. The extension makes VS Code an excellent Python editor, and works on any operating system with a variety of Python interpreters. It leverages all of VS Code’s power to provide auto complete and IntelliSense, linting, debugging, and unit testing, along with the ability to easily switch between Python environments, including virtual and conda environments.
This article provides only an overview of the different capabilities of the Python extension for VS Code. For a walkthrough of editing, running, and debugging code, use the button below.
Install Python and the Python extension
The tutorial guides you through installing Python and using the extension. You must install a Python interpreter yourself separately from the extension. For a quick install, use Python from python.org and install the extension from the VS Code Marketplace.
You can configure the Python extension through settings. Learn more in the Python Settings reference.
Insiders program
The Insiders program allows you to try out and automatically install new versions of the Python extension prior to release, including new features and fixes.
If you’d like to opt into the program, you can either open the Command Palette ( ⇧⌘P (Windows, Linux Ctrl+Shift+P ) ) and select Python: Switch to Insiders Daily/Weekly Channel or else you can open settings ( ⌘, (Windows, Linux Ctrl+, ) ) and look for Python: Insiders Channel to set the channel to «daily» or «weekly».
Run Python code
To experience Python, create a file (using the File Explorer) named hello.py and paste in the following code:
The Python extension then provides shortcuts to run Python code in the currently selected interpreter (Python: Select Interpreter in the Command Palette):
You can also use the Terminal: Create New Terminal command to create a terminal in which VS Code automatically activates the currently selected interpreter. See Environments below. The Python: Start REPL activates a terminal with the currently selected interpreter and then runs the Python REPL.
For a more specific walkthrough on running code, see the tutorial.
Autocomplete and IntelliSense
The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion (in-context method and variable suggestions) across all your files and for built-in and third-party modules.
Tip: Check out the IntelliCode extension for VS Code (preview). IntelliCode provides a set of AI-assisted capabilities for IntelliSense in Python, such as inferring the most relevant auto-completions based on the current code context.
Linting
Linting analyzes your Python code for potential errors, making it easy to navigate to and correct different problems.
The Python extension can apply a number of different linters including Pylint, pycodestyle, Flake8, mypy, pydocstyle, prospector, and pylama. See Linting.
Debugging
No more print statement debugging! Set breakpoints, inspect data, and use the debug console as you run your program step by step. Debug a number of different types of Python applications, including multi-threaded, web, and remote applications.
For Python-specific details, including setting up your launch.json configuration and remote debugging, see Debugging. General VS Code debugging information is found in the debugging document. The Django and Flask tutorials also demonstrate debugging in the context of those web apps, including debugging Django page templates.
Environments
The Python extension automatically detects Python interpreters that are installed in standard locations. It also detects conda environments as well as virtual environments in the workspace folder. See Configuring Python environments. You can also use the python.pythonPath setting to point to an interpreter anywhere on your computer.
The current environment is shown on the left side of the VS Code Status Bar:
The Status Bar also indicates if no interpreter is selected:
The selected environment is used for IntelliSense, auto-completions, linting, formatting, and any other language-related feature other than debugging. It is also activated when you use run Python in a terminal.
To change the current interpreter, which includes switching to conda or virtual environments, select the interpreter name on the Status Bar or use the Python: Select Interpreter command.
VS Code prompts you with a list of detected environments as well as any you’ve added manually to your user settings (see Configuring Python environments).
Installing packages
Packages are installed using the Terminal panel and commands like pip install
(Windows) and pip3 install
(macOS/Linux). VS Code installs that package into your project along with its dependencies. Examples are given in the Python tutorial as well as the Django and Flask tutorials.
Jupyter notebooks
You can also convert and open the notebook as a Python code file. The notebook’s cells are delimited in the Python file with #%% comments, and the Python extension shows Run Cell or Run All Cells CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:
Opening a notebook as a Python file allows you to use all of VS Code’s debugging capabilities. You can then save the notebook file and open it again as a notebook in the Notebook Editor, Jupyter, or even upload it to a service like Azure Notebooks.
Using either method, Notebook Editor or a Python file, you can also connect to a remote Jupyter server for running the code. For more information, see Jupyter support.
Testing
The Python extension supports testing with unittest and pytest.
To run tests, you enable one of the frameworks in settings. Each framework also has specific settings, such as arguments that identify paths and patterns for test discovery.
Once discovered, VS Code provides a variety of commands (on the Status Bar, the Command Palette, and elsewhere) to run and debug tests, including the ability to run individual test files and individual methods.
Configuration
The Python extension provides a wide variety of settings for its various features. These are described on their relevant topics, such as Editing code, Linting, Debugging, and Testing. The complete list is found in the Settings reference.
Other popular Python extensions
The Microsoft Python extension provides all of the features described previously in this article. Additional Python language support can be added to VS Code by installing other popular Python extensions.
The extensions shown above are dynamically queried. Click on an extension tile above to read the description and reviews to decide which extension is best for you. See more in the Marketplace.