Установка python на windows 10 vs 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.

Python Hello World Tutorial

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.

Note: To help get you started with Python development, you can use the Python profile template that includes useful extensions, settings, and Python code snippets.

Once you have a version of Python installed, select it using the Python: Select Interpreter command. If VS Code doesn’t automatically locate the interpreter you’re looking for, refer to Environments — Manually specify an interpreter.

You can configure the Python extension through settings. Learn more in the Python Settings reference.

Windows Subsystem for Linux: If you are on Windows, WSL is a great way to do Python development. You can run Linux distributions on Windows and Python is often already installed. When coupled with the WSL extension, you get full VS Code editing and debugging support while running in the context of WSL. To learn more, go to Developing in WSL or try the Working in WSL tutorial.

Run Python code

To experience Python, create a file (using the File Explorer) named hello.py and paste in the following code:

print("Hello World")

The Python extension then provides shortcuts to run Python code using the currently selected interpreter (Python: Select Interpreter in the Command Palette). To run the active Python file, click the Run Python File in Terminal play button in the top-right side of the editor.

You can also run individual lines or a selection of code with the Python: Run Selection/Line in Python Terminal command (Shift+Enter). If there isn’t a selection, the line with your cursor will be run in the Python Terminal. An identical Run Selection/Line in Python Terminal command is available on the context menu for a selection in the editor. The same terminal will be used every time you run a selection or a line in the terminal/REPL, until that terminal is closed.

The Python extension automatically removes indents based on the first non-empty line of the selection, shifting all other lines left as needed.

The command opens the Python Terminal if necessary; you can also open the interactive REPL environment directly using the Python: Start REPL command that activates a terminal with the currently selected interpreter and then runs the Python REPL.

For a more specific walkthrough and other ways of running code, see the run code 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.

IntelliSense quickly shows methods, class members, and documentation as you type. You can also trigger completions at any time with ⌃Space (Windows, Linux Ctrl+Space). Hovering over identifiers will show more information about them.

Enhance completions with AI

GitHub Copilot is an AI-powered code completion tool that helps you write code faster and smarter. You can use the GitHub Copilot extension in VS Code to generate code, or to learn from the code it generates.

GitHub Copilot provides suggestions for languages beyond Python and a wide variety of frameworks, including JavaScript, TypeScript, Ruby, Go, C# and C++.

You can learn more about how to get started with Copilot in the Copilot documentation.

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! VS Code comes with great debugging support for Python, allowing you to set breakpoints, inspect variables, and use the debug console for an in-depth look at how your program is executing step by step. Debug a number of different types of Python applications, including multi-threaded, web, and remote applications.

For more specific information on debugging in Python, such as configuring your launch.json settings and implementing remote debugging, see Debugging. General VS Code debugging information is found in the debugging document.

Additionally, the Django and Flask tutorials provide examples of how to implement debugging in the context of web applications, including debugging Django 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.

The current environment is shown on the right 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. It is also activated when you run or debug Python in a terminal, or when you create a new terminal with the Terminal: Create New Terminal command.

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).

Jupyter notebooks

To enable Python support for Jupyter notebook files (.ipynb) in VS Code, you can install the Jupyter extension. The Python and Jupyter extensions work together to give you a great Notebook experience in VS Code, providing you the ability to directly view and modify code cells with IntelliSense support, as well as run and debug them.

You can also convert and open the notebook as a Python code file through the Jupyter: Export to Python Script command. The notebook’s cells are delimited in the Python file with #%% comments, and the Jupyter extension shows Run Cell or Run Below CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:

You can also connect to a remote Jupyter server to run your notebooks. For more information, see Jupyter support.

Testing

The Python extension supports testing with Python’s built-in unittest framework and pytest.

In order to run tests, you must enable one of the supported testing frameworks in the settings of your project. Each framework has its own specific settings, such as arguments for identifying the paths and patterns for test discovery.

Once the tests have been discovered, VS Code provides a variety of commands (on the Status Bar, the Command Palette, and elsewhere) to run and debug tests. These commands also allow you to run individual test files and 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.

Python profile template

Profiles let you quickly switch your extensions, settings, and UI layout depending on your current project or task. To help you get started with Python development, you can use the Python profile template, which is a curated profile with useful extensions, settings, and snippets. You can use the profile template as is or use it as a starting point to customize further for you own workflows.

You select a profile template through the Profiles > Create Profile… dropdown:

Once you select a profile template, you can review the settings and extensions, and remove individual items if you don’t want to include them in your new Profile. After creating the new profile based on the template, changes made to settings, extensions, or UI are persisted in your profile.

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.

1. Open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)).
2. Filter the extension list by typing ‘python’.

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.

Next steps

• Python Hello World tutorial — Get started with Python in VS Code.
• Editing Python — Learn about auto-completion, formatting, and refactoring for Python.
• Basic Editing — Learn about the powerful VS Code editor.
• Code Navigation — Move quickly through your source code.
• Django tutorial
• Flask tutorial

3/22/2023

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.

To learn more about the Python language, follow any of the programming tutorials listed on python.org within the context of VS Code.

If you are looking 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 Hello World

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.

Configure and run the debugger

Let’s now try debugging our 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, 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:

1. 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
   

2. Now, rerun the program, with or without the debugger, to view the output!

Next steps

To learn to build web apps with the Django and Flask frameworks, see the following tutorials:

• Use Django in Visual Studio Code
• Use Flask 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

6/12/2023

Онлайн-компиляторы Python хороши, когда нужно быстро протестировать что-то простое, но для полноценной работы их недостаточно. Чтобы использовать всю мощь Python, нужно установить его на свой компьютер, и тогда можно подключать к нему любые библиотеки и писать код любой сложности.

В этой статье покажем, как установить Python под Windows и как с ним работать. Для MacOS всё почти то же самое, а если у вас Linux, то вы лучше нас знаете, как это сделать.

Скачивание и установка

Для начала нам нужно скачать установщик с официального сайта — python.org. Если качать Python с других сайтов, можно подцепить вирус или троян. Скачивайте программы только с официальных сайтов.

Несмотря на то, что Python 3 вышел 10 лет назад, до сих пор многие спорят про то, какую версию лучше использовать — вторую или третью. Мы за прогресс, поэтому качаем Python 3, но вы можете выбрать любую другую.

На главной странице сразу видим большую жёлтую кнопку, на которой написано «Download Python». Нажимаем, сохраняем и запускаем файл. Он весит около 25 мегабайт.

Когда установка закончится, нужно проверить, что всё было сделано правильно. Для этого в командной строке наберите py (латиницей) или python и нажмите клавишу ввода. Если всё хорошо, в ответ Python вам напишет номер своей версии и сборки и предложит несколько команд для знакомства с собой:

Запуск программ

Пока наш Python может работать только через командную строку — какие команды введёте, те он и выполнит. Многим разработчикам это нравится, но для старта это неудобно. Например, чтобы запустить программу, нужно написать в командной строке так:

py полное_имя_файла

Полное имя означает, что нужно написать не только название файла, но и диск с папкой, где он находится. Чтобы было понятнее, давайте возьмём наш код из статьи про таймер на Python и сохраним его в файле time.py3 на диске D. Py3 означает, что внутри этого файла будет код на Python3. Можно просто назвать файл python.py, без тройки, но для некоторых моментов это может быть важно.

Теперь, чтобы запустить наш код, напишем в командной строке:

py D:\time.py3

Результат работы — выполненный алгоритм:

Подключаем VS Code

Мы уже рассказывали об этом редакторе кода — он быстрый, бесплатный и есть много плагинов. Чтобы работать с Python-файлами было проще, научим VS Code понимать и запускать их. Для этого нам нужно найти и установить специальный плагин для этого языка. Открываем вкладку Расширения (Extensions) и пишем такое:

tag:debuggers @sort:installs

В результатах поиска находим Python и нажимаем Install:

Затем то же самое делаем для подсветки синтаксиса: пишем в Расширениях команду ext:py3 и устанавливаем плагин MagicPython. После этого перезапускаем VS Code, открываем наш файл и нажимаем F5. Когда выпадающее окошко спросит, с чем будем работать — выбираем Python. В итоге мы получаем редактор кода с нужной нам подсветкой синтаксиса и результатом работы в том же окне. Красота!