Как установить matplotlib в visual studio 2019
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:
- Write, run, and debug a Python "Hello World" Application
- Learn how to install packages by creating Python virtual environments
- Write a simple Python script to plot figures within VS Code
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:
- VS Code
- VS Code Python extension
- Python 3
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
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, Python 3.9, and Python 3.10. Be aware that you might have compatibility issues with some packages using this method.
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.
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 Remote - 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.
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.
Note 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 project (workspace) folder
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:
Note: If you're using an Anaconda distribution, be sure to use an Anaconda command prompt.
By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in .vscode/settings.json , which are separate from user settings that are stored globally.
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.
Note: When using an Anaconda distribution, the correct interpreter should have the suffix ('base':conda) , for example Python 3.7.3 64-bit ('base':conda) .
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:
Name the file hello.py , and it automatically opens 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 :
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. :
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.
First, set a breakpoint on line 2 of hello.py by placing the cursor on the print call and pressing F9 . Alternately, just 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.
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.
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 Hello world 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:
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.)
Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called standardplot.py , and paste in the following source code:
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, 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 to build web apps with the Django and Flask frameworks, see the following tutorials:
Чтобы установить поддержку Python для Visual Studio (Инструменты Python для Visual Studio или PTVS), выполните инструкции из раздела, который соответствует вашей версии Visual Studio:
Чтобы быстро проверить поддержку Python после установки, откройте интерактивное окно Python. Для этого нажмите клавиши ALT+I и введите 2+2 . Если вы не увидите результат 4 , проверьте выполненные действия.
Рабочая нагрузка Python содержит полезное расширение Cookiecutter, которое предоставляет графический пользовательский интерфейс для поиска шаблонов, ввода параметров шаблонов и создания проектов и файлов. Дополнительные сведения см. в статье Использование расширения Cookiecutter.
Сейчас Python не поддерживается в Visual Studio для Mac, однако доступен в Mac и Linux посредством Visual Studio Code. См. вопросы и ответы.
Visual Studio 2022
Visual Studio 2019
Visual Studio 2017
Скачайте и запустите последнюю версию Visual Studio Installer. Если вы уже установили Visual Studio, запустите Visual Studio Installer, выберите вариант Изменить (см. раздел Изменение Visual Studio) и перейдите к шагу 2.
Выпуск Community предназначен для индивидуальных разработчиков, использования при аудиторном обучении и в научных исследованиях, а также разработки решений с открытым кодом. Если программу планируется использовать в других целях, установите Visual Studio 2022 Professional или Visual Studio 2022 Enterprise.
Выпуск Community предназначен для индивидуальных разработчиков, использования при аудиторном обучении и в научных исследованиях, а также разработки решений с открытым кодом. Если программу планируется использовать в других целях, установите Visual Studio 2019 Professional или Visual Studio 2019 Enterprise.
Установщик Visual Studio предоставляет список рабочих нагрузок, которые представляют собой группы связанных параметров для конкретных областей разработки. Для Python выберите рабочую нагрузку Разработка на Python.
.
В правой части установщика при необходимости выберите другие параметры. Чтобы принять параметры по умолчанию, пропустите этот шаг.
После установки в установщике предлагаются команды для изменения, запуска, восстановления и удаления Visual Studio. Если доступны обновления для установленных компонентов Visual Studio, кнопка Изменить меняется на Обновить. (Команду Изменить в этом случае можно выбрать в раскрывающемся меню.) Запускать среду Visual Studio и установщик можно также из меню Пуск в Windows. Для этого выполните поиск по запросу "Visual Studio".
Рабочие нагрузки для Python и обработки и анализа данных доступны только в Visual Studio2017 версии 15.2 и выше.
Диагностика
Чтобы устранить проблемы при установке или запуске Python в Visual Studio, попробуйте сделать следующее:
- Определите, возникает ли та же ошибка при использовании Python CLI, то есть при запуске python.exe из командной строки.
- Воспользуйтесь восстановлением Visual Studio.
- Восстановите или переустановите Python через Параметры >Приложения и компоненты в Windows.
Visual Studio 2015
Чтобы запустить установщик Visual Studio, откройте Панель управления > Программы и компоненты, затем выберите Microsoft Visual Studio 2015 и нажмите кнопку Изменить.
В самом установщике выберите действие Изменить.
Выберите Языки программирования > Инструменты Python для Visual Studio и нажмите Далее:
Visual Studio 2013 и более ранние версии
Установите версию Инструментов Python для Visual Studio, соответствующую вашей версии Visual Studio.
- Visual Studio 2013: PTVS 2.2.2 для Visual Studio 2013 В Visual Studio 2013 этот процесс можно запустить через диалоговое окно Файл >Новый проект.
- Visual Studio 2010 и 2012: PTVS 2.1.1 для Visual Studio 2010 и 2012
Selecting and installing Python interpreters (Установка и настройка интерпретаторов Python). Если интерпретатор уже установлен, но Visual Studio не обнаруживает его автоматически, см. руководство по определению существующего окружения вручную.
Расположения установки
По умолчанию поддержка Python устанавливается для всех пользователей на компьютере.
В Visual Studio 2022 рабочая нагрузка Python устанавливается в каталог %ProgramFiles%\Microsoft Visual Studio\\Common7\IDE\Extensions\Microsoft\Python, где — это 2022, а — Community, Professional или Enterprise.
По умолчанию поддержка Python устанавливается для всех пользователей на компьютере.
В Visual Studio 2019 и Visual Studio 2017 рабочая нагрузка Python устанавливается в каталог %ProgramFiles(x86)%\Microsoft Visual Studio\\Common7\IDE\Extensions\Microsoft\Python, где — это 2019 или 2017, а — Community, Professional или Enterprise.
В Visual Studio 2015 и более ранних версиях используются такие пути установки:
- 32-разрядная версия.
- Путь: %Program Files(x86)%\Microsoft Visual Studio \Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\
- Расположение пути в реестре: HKEY_LOCAL_MACHINE\Software\Microsoft\PythonTools\\InstallDir
- Путь: %Program Files%\Microsoft Visual Studio \Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\
- Расположение пути в реестре: HKEY_LOCAL_MACHINE\Software\Wow6432Node\Microsoft\PythonTools\\InstallDir
- принимает одно из этих значений:
- 14.0 for Visual Studio 2015
- 12.0 for Visual Studio 2013
- 11.0 for Visual Studio 2012
- 10.0 for Visual Studio 2010
Пользовательские установки (для версии 1.5 и более ранних)
Инструменты Python для Visual Studio 1.5 и более ранних версий можно установить только для текущего пользователя. Путь установки будет выглядеть так: %LocalAppData%\Microsoft\VisualStudio\\Extensions\Microsoft\Python Tools for Visual Studio\ . Здесь и имеют то же значение, которое описано выше.
The Python developer community has produced thousands of useful packages that you can incorporate into your own projects. Visual Studio provides a UI to manage packages in your Python environments.
View environments
Select the View > Other Windows > Python Environments menu command. The Python Environments window opens as a peer to Solution Explorer and shows the different environments available to you. The list shows both environments that you installed using the Visual Studio installer and environments you installed separately. That includes global, virtual, and conda environments. The environment in bold is the default environment that's used for new projects. For more information about working with environments, see How to create and manage Python environments in Visual Studio environments.
You can also use the Ctrl+K, Ctrl+` keyboard shortcut to open the Python Environments window from the Solution Explorer window. If the shortcut doesn't work and you can't find the Python Environments window in the menu, it's possible that you haven't installed the Python workload. See How to install Python support in Visual Studio on Windows for guidance about how to install Python.
With a Python project open, you can open the Python Environments window from Solution Explorer. Right-click Python Environments and select View All Python Environments.
Now, create a new project with File > New > Project, selecting the Python Application template.
In the code file that appears, paste the following code, which creates a cosine wave like the previous tutorial steps, only this time plotted graphically. You can also use the project you previously created and replace the code.
In the editor window, hover over the numpy and matplotlib import statements. You'll notice that they aren't resolved. To resolve the import statements, install the packages to the default global environment.
When you look at the editor window, notice that when you hover over the numpy and matplotlib import statements that they aren't resolved. The reason is the packages haven't been installed to the default global environment.
For example, select Open interactive window and an Interactive window for that specific environment appears in Visual Studio.
The Packages tab in the Python Environments window lists all packages that are currently installed in the environment.
Install packages using the Python Environments window
From the Python Environments window, select the default environment for new Python projects and choose the Packages tab. You'll then see a list of packages that are currently installed in the environment.
Install matplotlib by entering its name into the search field and then selecting the Run command: pip install matplotlib option. Running the command will install matplotlib , and any packages it depends on (in this case that includes numpy ).
Choose the Packages tab.
Consent to elevation if prompted to do so.
After the package is installed, it appears in the Python Environments window. The X to the right of the package uninstalls it.
Enter matplotlib into the search field to install matplotlib .
Select the Run command: pip install matplotlib option. This option installs matplotlib , and any packages it depends on (in this case, that includes numpy ).
Consent to elevation if prompted to do so.
After the package installs, it appears in the Python Environments window. The X to the right of the package uninstalls it.
A small progress bar might appear underneath the environment to indicate that Visual Studio is building its IntelliSense database for the newly-installed package. The IntelliSense tab also shows more detailed information. Be aware that until that database is complete, IntelliSense features like auto-completion and syntax checking won't be active in the editor for that package.
Visual Studio 2017 version 15.6 and later uses a different and faster method for working with IntelliSense, and displays a message to that effect on the IntelliSense tab.
Run the program
Now that matplotlib is installed, run the program with (F5) or without the debugger (Ctrl+F5) to see the output:
Сообщество разработчиков на Python создало тысячи полезных пакетов, которые вы можете включать в свои проекты. В Visual Studio имеется пользовательский интерфейс для управления пакетами в средах Python.
Просмотр окружений
Выберите команду меню Просмотр > Другие окна > Окружения Python. Откроется окно Окружения Python (как узел обозревателя решений), в котором представлены разные среды, доступные вам. Список содержит как окружения, установленные с помощью установщика Visual Studio, так и окружения, которые вы установили отдельно. В их число входят глобальные, виртуальные среды и среды Conda. Среда, выделенная полужирным шрифтом, — это среда, используемая по умолчанию для новых проектов. Дополнительные сведения о работе со окружениями см. в разделе Создание окружений Python и управление ими в средах Visual Studio.
Используйте сочетания клавиш CTRL +K, CTRL +` , чтобы открыть окно Окружения Python из окна Обозревателя решений. Если сочетание клавиш не работает и окно "Окружения Python" отсутствует в меню, возможно, не установлена рабочая нагрузка Python. Инструкции по установке Python см. в статье Установка поддержки Python в Visual Studio в Windows.
Если открыт проект Python, вы можете открыть окно Окружения Python из Обозревателя решений. Щелкните правой кнопкой мыши Окружения Python и выберите пункт Просмотреть все окружения Python.
Теперь создайте проект, выбрав пункт меню Файл > Создать > Проект, а затем выбрав шаблон Приложение Python.
В появившийся файл кода вставьте приведенный ниже код, который строит косинусоиду, как в предыдущих шагах учебника, но теперь в виде графика. Можно также использовать ранее созданный проект и заменить код.
В окне редактора наведите указатель мыши на инструкции импорта numpy и matplotlib . Вы заметите, что они не разрешены. Чтобы разрешить инструкции импорта, установите пакеты в глобальное окружение по умолчанию.
Если в окне редактора навести указатель мыши на операторы импорта numpy и matplotlib , вы заметите, что они не разрешены. Это связано с тем, что пакеты не были установлены в глобальном окружении по умолчанию.
Например, щелкните ссылку Открыть интерактивное окно, и в Visual Studio откроется интерактивное окно для этого окружения.
На вкладке Пакеты в окне "Окружения Python" указаны все пакеты, установленные в настоящий момент в окружении.
Установка пакетов с помощью окна "Окружения Python"
В окне "Окружения Python" выберите окружение по умолчанию для новых проектов Python и перейдите на вкладку Пакеты. Вы увидите список пакетов, которые в настоящее время установлены в окружении.
Установите пакет matplotlib , введя его имя в поле поиска, а затем выбрав параметр Выполнить команду "pip install matplotlib" . При выполнении этой команды будут установлены пакет matplotlib , а также все пакеты, от которых он зависит (в данном случае — numpy ).
Выберите вкладку Пакеты.
Согласитесь на повышение прав, если появится соответствующий запрос.
Установленный пакет появится в окне Окружения Python. Если щелкнуть знак X справа от пакета, он будет удален.
Введите matplotlib в поле поиска для установки matplotlib .
Выберите вариант Выполнить команду: pip install matplotlib. Будет установлен пакет matplotlib , а также все пакеты, от которых он зависит (в данном случае — numpy ).
Согласитесь на повышение прав, если появится соответствующий запрос.
Установленный пакет появится в окне Окружения Python. Если щелкнуть знак X справа от пакета, он будет удален.
Под названием среды может появиться небольшой индикатор выполнения, который указывает на то, что Visual Studio создает базу данных IntelliSense для нового пакета. На вкладке IntelliSense также приводятся более подробные сведения. Имейте в виду, что, пока база данных не будет готова, функции IntelliSense, такие как автозавершение и проверка синтаксиса, будут неактивны для этого пакета в редакторе.
Запуск программы
После установки matplotlib запустите программу с отладчиком (F5) или без него (CTRL+F5), чтобы увидеть результат.
The Coding Pack for Python helps you quickly set up a Python coding environment with Visual Studio Code. The standalone installer helps you install a Python interpreter, Visual Studio Code, extensions that provide support for Python in Visual Studio Code, and a number of common and useful Python packages.
Getting started
With the Coding Pack for Python, it's easy to get started developing with Python and VS Code.
Download and run the Coding Pack for Python installer.
Note: The installer only supports Windows 10 64-bit. This download is 200MB, and up to 100MB will be downloaded while you are installing.
Once the installer launches, review and accept the License Agreement. Then select Install.
After installation completes, select Next.
Note: If you select Cancel before the installation completes, you will need to manually remove and uninstall any components that have already been installed.
Launch Visual Studio Code and start coding!
Note: If there are any issues installing components, you can use the steps discussed in Manual installation
What's installed by the Coding Pack for Python
The Coding Pack for Python installs the key components you need to use Visual Studio Code for Python development. Specifically, it installs:
- Visual Studio Code
- Visual Studio Code extensions:
- Python
- Pylance
- Live Share
- Gather
- jupyter
- numpy
- sklearn
- pandas
- Matplotlib
Along with the tools and packages necessary for Python development, the Coding Pack also configures common user settings and PowerShell. This includes Python extension settings, such as the default interpreter and language server, as well as execution policies to allow for virtual environment activation in the terminal.
Note: If there was an existing version of Visual Studio Code installed on your machine, your settings.json will not be overwritten and you'll need to configure Python settings yourself.
Manual installation
If you have any problems during installation, the following manual steps can be used to complete your installation.
Visual Studio Code and the Python extension
If there was an issue installing VS Code, you can install it from here.
Once VS Code is installed, you can install the Python extension for VS Code from the Visual Studio Marketplace. For additional details about installing extensions, see Extension Marketplace. The Python extension is named Python and is published by Microsoft.
Python interpreter
If there was an issue installing the Python interpreter, you can install Python 3.8 from the Microsoft Store. Along with the Python extension, you need to install a Python interpreter for development with Python. There are other options for installing the Python interpreter, such as directly from Python.org, and which interpreter you use is dependent on your specific needs. If you use the Python.org version, just make sure to uncheck the "Install launcher for all users" box if you don't have admin access.
Note: If you use the Microsoft Store installation option, be aware that some packages might not work well with this package; however, the packages listed below have been tested and work fine.
- Verify your Python installation by opening a cmd prompt and running the following code python --version . If the installation was successful, the output window shows the version of Python that you just installed.
Additional VS Code extensions
Pylance language server extension
Pylance is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool. Using Pyright, Pylance can supercharge your Python IntelliSense experience with rich type information, helping you write better code faster.
- Install the Pylance extension from the Visual Studio marketplace.
- Open a Python (.py) file and the Pylance extension will activate.
- Select Yes when prompted to make Pylance the default language server. This will update your preferences, which you can also do manually by adding "python.languageServer": "Pylance" to your settings.json file using the text editor.
Gather extension
The Gather extension adds the experimental Gather feature to the Python extension. With one button, you'll be able to select any notebook or Interactive Window cell and have Gather find and then copy all of the dependent code that was used to generate that cell's result into a new notebook or script.
Live Share extension
Visual Studio Live Share enables you to collaboratively edit and debug with others in real time, regardless of what programming languages you're using or app types you're building. It allows you to instantly share your current project, and then as needed, share debugging sessions, terminal instances, localhost web apps, voice calls, and more! For additional details, see the documentation.
-
and install the Visual Studio Live Share extension from the Visual Studio marketplace.
- Follow the guidance in the documentation about How-to: Collaborate using Visual Studio Code
- Open VS Code
- Within VS Code, open the Command Palette (ctrl+shift+p)
- Select Python: Select Interpreter
- Select the interpreter that you installed or that was installed by the Coding Pack
- Install the Pylance extension and set it as the default language server as described in the section above.
- Open VS Code
- If a terminal is not already opened, select Terminal > New Terminal from the main toolbar
- Once the terminal has opened, enter the following command: Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned
- The folder with the Python interpreter and associated Python packages will be removed, including any user installed packages.
- The folder will be removed from the PATH environment variable.
- Any settings modified by the installation process will be reset.
Common Python packages
If you need to manually install the Python packages that the Coding Pack would otherwise have installed, you can do so using the following Python commands.
Be aware that these commands will install the packages into the global environment for your interpreter, because that's where the Coding Pack would have installed them. That said, a good option to consider is adding the packages to a virtual environment. For information about virtual environments, see the topic Using Python environments in VS Code.
Note: If you have problems running the Python commands above, you might need to make sure that the Python interpreter is on your PATH environment variable.
Settings and configuration
To help you get started quickly, the Coding Pack for Python sets a few key settings. If you need to configure them manually, you can use the following guidance.
Set default interpreter
Set language server to pylance
Enable running scripts in PowerShell
Uninstalling the Coding Pack for Python
If you need to uninstall (or repair) your Coding Pack for Python installation, you can use the following steps.
Rerun the standalone installer.
At the UI prompt, select Uninstall.
Once you select uninstall, the following tasks will be performed:
Note: The uninstall process will not remove Visual Studio Code. At the end of the uninstall process, you can click the provided link to open "Apps & features" to uninstall Visual Studio Code. If you decide to repair your installation, be aware that any other Python packages you might have installed will be removed as part of the repair process.
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