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Home » Setup Vscode For Python | Install Python And The Python Extension

Setup Vscode For Python | Install Python And The Python Extension

How to set up Python on Visual Studio Code

Mypy Type Checker

Before I start talking about this extension, let me explain what mypy actually is.

According to the info on their homepage:

Mypy is an optional static type checker for Python that aims to combine the benefits of dynamic (or “duck”) typing and static typing. Mypy combines the expressive power and convenience of Python with a powerful type system and compile-time type checking.

In simpler words, mypy forces you to add essential type annotations to your Python programs, making them easier to comprehend.

Recently, Microsoft has published an extension that adds type checking functionality using mypy to their beloved editor.

Once you have installed the extension, it’ll perform necessary checks on your code and report any missing type annotations as compile-time errors.

While having type annotations is not mandatory, it’s highly recommended.

Visual Studio Code Python for Data Science

Visual Studio Code allows users to simply run the data science code in Jupyter Notebook. We can run the cell and visualize the result within VSCode. It supports all kinds of programming languages and comes with built-in features to mimic the browser-based Jupyter notebook that we all love.

Learn more about Jupyter Notebooks by reading our How to use Jupyter Notebook tutorial.

To use the Jupyter notebook extension, we need to first install a Jupyter notebook.


pip install jupyterlab

Or


pip install notebook

Note: Jupyter Notebook and Jupyter Lab come with Anaconda Distribution, so we don’t have to install anything.

Install Jupyter Extension

After that, install the Jupyter extension from the Visual Studio marketplace.

To create a Jupyter notebook file, we can either create a new file with .ipynb extension or access the command palette (Ctrl+Shift+P) and select Jupyter: Create New Jupyter Notebook.

Pick the Ipython Kernel

To initialize the Jupyter server, we need to select the kernel by clicking on the kernel picker in the top right of the notebook, as shown in the image.

Note: By default, Anaconda comes with Python version 3.9.13. You can download the latest version of Python 3.11, but it won’t support all packages.

Run the Jupyter cell

Write a print expression to display “Hello World” and press the run button.

Add another cell

You can use the B key or click on + Code to add a new cell and run the cell with Ctrl + ⤶ Enter. You can learn about Jupyter keyboard shortcuts on defkey.

For R language users, we have got a Notebooks for R tutorial. You will learn to use R in a Jupyter Notebook and useful features.

Note: if you are looking for a hassle-free way of using Jupyter Notebook, then try DataCamp Workspace. It comes with essential Python libraries, a pre-build environment, and it supports various database integration.

How to set up Python on Visual Studio Code
How to set up Python on Visual Studio Code

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.

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 (*).

How to setup Python for VSCode in 2023 in 5mins! | Install Python and Setup VSCode for Windows 10
How to setup Python for VSCode in 2023 in 5mins! | Install Python and Setup VSCode for Windows 10

Indent Rainbow

Unlike other programming languages, an incorrect level of indentation can literally break your program in Python.

Visual Studio Code already does a good job of visualizing indentation levels within your code, but if you want to add some color to it, the indent-rainbow package is what you need.

It adds different colors to the different levels of indentation. Personally, I don’t use this one on a regular basis, but you may find it useful.

Debugging

No more

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.

How To Setup Python for VSCode | Setting Up VSCode For Python Programming
How To Setup Python for VSCode | Setting Up VSCode For Python Programming

Environment variables

Environment variable definitions file

An environment variable definitions file is a text file containing key-value pairs in the form of

environment_variable=value

, with used for comments. Multiline values aren’t supported, but references to previously defined environment variables are allowed. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but aren’t applied to the terminal.

Note: Environment variable definitions files are not necessarily cross-platform. For instance, while Unix uses

as a path separator in environment variables, Windows uses. There is no normalization of such operating system differences, and so you need to make sure any environment definitions file use values that are compatible with your operating system.

By default, the Python extension looks for and loads a file named

.env

in the current workspace folder, then applies those definitions. The file is identified by the default entry

"python.envFile": "${workspaceFolder}/.env"

in your user settings (see General Python settings). You can change the

python.envFile

setting at any time to use a different definitions file.

Note: Environment variable definitions files are not used in all situations where environment variables are available for use. Unless Visual Studio Code documentation states otherwise, these only affect certain scenarios as per their definition. For example, the extension doesn’t use environment variable definitions files when resolving setting values.

A debug configuration also contains an

envFile

property that also defaults to the

.env

file in the current workspace (see Debugging – Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default

.env

file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

dev.env file


# dev.env - development configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/dev/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/dev MYPROJECT_DBUSER=devadmin MYPROJECT_DBPASSWORD=!dfka**213=

prod.env file


# prod.env - production configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/ MYPROJECT_DBUSER=coreuser MYPROJECT_DBPASSWORD=kKKfa98*11@

You can then set the

python.envFile

setting to

${workspaceFolder}/prod.env

, then set the

envFile

property in the debug configuration to

${workspaceFolder}/dev.env

.

Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. All


env

variables defined in the

launch.json

file will override variables contained in the

.env

file, specified by the

python.envFile

setting (user or workspace). Similarly,

env

variables defined in the

launch.json

file will override the environment variables defined in the

envFile

that are specified in

launch.json

.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (

terminal.integrated.env.*

) and/or within an

.env

file.

When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn’t routed through the terminal, such as the use of a linter or formatter, then this setting won’t have an effect on module look-up.

Why use VSCode for Python?

Virtual Studio Code (VSCode) is a perfect Integrated Development Environment for Python. It is simple and comes with built-in features that enhance the development experience. VSCode Python extensions come with powerful features like syntax autocomplete, linting, debugging, unit testing, GitOps, virtual environments, notebooks, editing tools, and the ability to customize the editor.

Key Features:

  1. Command Palette to access all commands by typing keywords.
  2. Fully customizable keyboard shortcuts.
  3. Jupyter extension for data science. Run Jupyter notebook within the IDE.
  4. Auto linting and formatting.
  5. Debugging and Testing.
  6. Git integration.
  7. Custom code snippets.
  8. Enhanced editing tools. Multi cursor selection, column selection, outline view, side-by-side preview, and search and modify.

In this tutorial, we will start by installing Python and VSCode, then run a Python script in VSCode. After that, we will customize the editor to enhance the Python development experience by installing essential extensions and learning about built-in features. In the end, we will learn about Python productivity hacks.

Python and Visual Studio Code Installation
Python and Visual Studio Code Installation

Conclusion

VSCode is not just a code editor. It is a complete ecosystem for efficient Python development. It provides us with shortcuts, Commands Palette, IntelliSense, linting, formatting, debugging, formatting, Git integrations, Jupyter notebook, third-party extensions, and a fully customizable development experience.

VSCode is highly recommended to beginners who are learning the basics of Python and data science. Complete Data Scientist with a Python career track to become a master in Python and data science. The career track consists of 25 courses and six projects to prepare you to become a professional data scientist.

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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.

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.

How To Setup A Virtual Environment For Python In Visual Studio Code In 2023
How To Setup A Virtual Environment For Python In Visual Studio Code In 2023

IntelliCode

IntelliCode provides AI assisted code completion in Visual Studio Code. It may sound similar to GitHub Copilot, but in reality it’s a lot smaller than that.

Where GitHub Copilot or Tabnine provides full-blown code blocks, IntelliCode autocompletes lines of code pretty flawlessly.

In most cases, this extension can help you type less of the same code by suggesting the right thing while also keeping out of your way.

Conclusion

Visual Studio Code is one of the coolest general purpose editors and a great candidate for Python development. In this article, you learned:

  • How to install VS Code on any platform
  • How to find and install extensions to enable Python-specific features
  • How VS Code makes writing a simple Python application easier
  • How to run and debug existing Python programs within VS Code
  • How to work with Git and GitHub repositories from VS Code

Visual Studio Code has become my default editor for Python and other tasks, and I hope you give it a chance to become yours as well.

If you have questions or comments, please reach out in the comments below. There is also a lot more information at the Visual Studio Code website than we could cover here.

The author sends thanks to Dan Taylor from the Visual Studio Code team at Microsoft for his time and invaluable input in this article.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Development in Visual Studio Code (Setup Guide)

Quick Start Guide for Python in VS Code

The Python extension makes Visual Studio Code an excellent Python editor, works on any operating system, and is usable with a variety of Python interpreters.

Get started by installing:

  • VS Code
  • A Python Interpreter (any actively supported Python version)
  • Python extension from the VS Code Marketplace

To further customize VS Code for Python, you can leverage the Python profile template, automatically installing recommended extensions and settings. For Data Science projects, consider using the Data Science profile template.

Powerful VSCode Tips And Tricks For Python Development And Design
Powerful VSCode Tips And Tricks For Python Development And Design

Git Integration

VS Code has built-in support for source control management, and ships with support for Git and GitHub right out of the box. You can install support for other SCM’s in VS Code, and use them side by side. Source control is accessible from the Source Control view:

If your project folder contains a

.git

folder, VS Code automatically turns on the full range of Git/GitHub functionality. Here are some of the many tasks you can perform:

  • Commit files to Git
  • Push changes to, and pull changes from, remote repos
  • Check-out existing or create new branches and tags
  • View and resolve merge conflicts
  • View diffs

All of this functionality is available directly from the VS Code UI:

VS Code will also recognize changes made outside the editor and behave appropriately.

Committing your recent changes within VS Code is a fairly straightforward process. Modified files are shown in the Source Control view with an M marker, while new untracked files are marked with a U. Stage your changes by hovering over the file and then clicking the plus sign (+). Add a commit message at the top of the view, and then click the check mark to commit the changes:

You can push local commits to GitHub from within VS Code as well. Select Sync from the Source Control view menu, or click Synchronize Changes on the status bar next to the branch indicator.

Testing Support

VS Code can automatically recognize existing Python tests written in the

unittest

framework, or the

pytest

or

Nose

frameworks if those frameworks are installed in the current environment. I have a unit test written in

unittest

for the equation eval library, which you can use for this example.

To run your existing unit tests, from any Python file in the project, right-click and select Run Current Unit Test File. You’ll be prompted to specify the test framework, where in the project to search for tests, and the filename pattern your tests utilize.

All of these are saved as workspace settings in your local

.vscode/settings.json

file and can be modified there. For this equation project, you select

unittest

, the current folder, and the pattern

*_test.py

.

Once the test framework is set up and the tests have been discovered, you can run all your tests by clicking Run Tests on the Status Bar and selecting an option from the Command Palette:

You can even run individual tests by opening the test file in VS Code, clicking Run Tests on the Status Bar, and selecting the Run Unit Test Method… and the specific test to run. This makes it trivial to address individual test failures and re-run only failed tests, which is a huge time-saver! Test results are shown in the Output pane under Python Test Log.

Python Full Course for Beginners | Complete All-in-One Tutorial | 9 Hours
Python Full Course for Beginners | Complete All-in-One Tutorial | 9 Hours

Configuring extensions

VS Code extensions may have very different configurations and requirements. Some extensions contribute settings to VS Code, which can be modified in the Settings editor. Other extensions may have their own configuration files. Extensions may also require installation and setup of additional components like compilers, debuggers, and command-line tools. Consult the extension’s README (visible in the Extensions view details page) or go to the extension page on the VS Code Marketplace (click on the extension name in the details page). Many extensions are open source and have a link to their repository on their Marketplace page.

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 (*).

How To Install Python Libraries In Visual Studio Code (Windows 11)
How To Install Python Libraries In Visual Studio Code (Windows 11)

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!

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!

Debugging

No more

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.

How to install Visual Studio Code on Windows 10/11 [ 2024 Update ] Complete Guide
How to install Visual Studio Code on Windows 10/11 [ 2024 Update ] Complete Guide

Next steps

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

There is 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.

Visual Studio Code is one of the most versatile code editors out there. Even though it’s a code editor, the sheer extensibility of the program makes it almost as capable as some of the JetBrains products out there.

In this article, I’ll walk you through the entire process of configuring Visual Studio Code for Python development. It’s not a universal setup, but this is something that I use personally and have found it to be really comfortable.

The first step is to install Visual Studio Code on your computer. I’m on Debian 12 at the moment and I have the editor ready to go. Platform specific installation instructions are available in the documentation.

Assuming you are past the installation step, now I’ll introduce you to a set of essential extensions that will elevate your Python development experience to the next level.

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

Harvard CS50’s Introduction to Programming with Python – Full University Course
Harvard CS50’s Introduction to Programming with Python – Full University Course

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 same terminal is also used for Run Python File in Terminal. If that terminal is still running the REPL, you should exit the REPL (

exit()

) or switch to a different terminal before running a Python file.

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.

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 same terminal is also used for Run Python File in Terminal. If that terminal is still running the REPL, you should exit the REPL (

exit()

) or switch to a different terminal before running a Python file.

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.

How to install Python 3.12.0 on Windows 11
How to install Python 3.12.0 on Windows 11

Ruff Linter

A linter is a program that analyses your code statically and provides valuable insights on possible errors.

The Pylance extension does an excellent job of finding out fatal errors within your code, but there is more to code than just that.

When working on a big project, it’s pretty common to leave unwanted mess within your codebase. Things like unused imports and variables, bad code practices, and so on.

A good linter can point out code smells like this and make your code cleaner. Now, the go-to choice when it comes to Python linters is Pylint.

Pylint has been around for ages and works quite well, but I think there is a better alternative.

Ruff is an extremely fast Python linter written in Rust that imposes stricter linting rules than Pylint. The tool also has an official extension.

It’s a plug n’ play extension and doesn’t require any additional configuration whatsoever. So once you have it installed, you’re good to go.

Start a New Python Program

Let’s start our exploration of Python development in Visual Studio Code with a new Python program. In VS Code, type Ctrl+N to open a new File. (You can also select File, New from the menu.)

Note: The Visual Studio Code UI provides the Command Palette, from which you can search and execute any command without leaving the keyboard. Open the Command Palette using Ctrl+Shift+P, type

File: New File

, and hit Enter to open a new file.

No matter how you get there, you should see a VS Code window that looks similar to the following:

Once a new file is opened, you can begin entering code.

Entering Python Code

For our test code, let’s quickly code up the Sieve of Eratosthenes (which finds all primes less than a given number). Begin typing the following code in the new tab you just opened:


sieve = [True] * 101 for i in range(2, 100):

You should see something similar to this:

Wait, what’s going on? Why isn’t Visual Studio Code doing any keyword highlighting, any auto-formatting, or anything really helpful? What gives?

The answer is that, right now, VS Code doesn’t know what kind of file it’s dealing with. The buffer is called

Untitled-1

, and if you look in the lower right corner of the window, you’ll see the words Plain Text.

To activate the Python extension, save the file (by selecting File, Save from the menu, File:Save File from the Command Palette, or just using Ctrl+S) as

sieve.py

. VS Code will see the

.py

extension and correctly interpret the file as Python code. Now your window should look like this:

That’s much better! VS Code automatically reformats the file as Python, which you can verify by inspecting the language mode in the lower left corner.

If you have multiple Python installations (like Python 2.7, Python 3.x, or Anaconda), you can change which Python interpreter VS Code uses by clicking the language mode indicator, or selecting Python: Select Interpreter from the Command Palette. VS Code supports formatting using

pep8

by default, but you can select

black

or

yapf

if you wish.

Let’s add the rest of the Sieve code now. To see IntelliSense at work, type this code directly rather than cut and paste, and you should see something like this:

Here’s the full code for a basic Sieve of Eratosthenes:


sieve = [True] * 101 for i in range(2, 100): if sieve[i]: print(i) for j in range(i*i, 100, i): sieve[j] = False

As you type this code, VS Code automatically indents the lines under

for

and

if

statements for you properly, adds closing parentheses, and makes suggestions for you. That’s the power of IntelliSense working for you.

Running Python Code

Now that the code is complete, you can run it. There is no need to leave the editor to do this: Visual Studio Code can run this program directly in the editor. Save the file (using Ctrl+S), then right-click in the editor window and select Run Python File in Terminal:

You should see the Terminal pane appear at the bottom of the window, with your code output showing.

Python Linting Support

You may have seen a pop up appear while you were typing, stating that linting was not available. You can quickly install linting support from that pop up, which defaults to PyLint. VS Code also supports other linters. Here’s the complete list at the time of this writing:


  • pylint

  • flake8

  • mypy

  • pydocstyle

  • pep8

  • prospector

  • pyllama

  • bandit

The Python linting page has complete details on how to setup each linter.

Note: The choice of linter is a project workspace setting, and not a global user setting.

You MUST WATCH THIS before installing PYTHON. PLEASE DON'T MAKE this MISTAKE.
You MUST WATCH THIS before installing PYTHON. PLEASE DON’T MAKE this MISTAKE.

Python and Visual Studio Code Setup

In this part, we will learn to install Python and VSCode and run a simple Python code.

Installing Python

Downloading and installing the latest version of Python is straightforward. Go to Python.org and download the latest version for Windows. The installer is also available for Linux/Unix, macOS, and other platforms. After downloading the installer, install Python with default settings.

Image from Python.org

The most popular way of installing Python is through Anaconda Distribution. It comes with a pre-installed package and software for us to start coding without hiccups. It is available for Windows, macOS, and Linux operating systems.

Image from Anaconda

After installing Python on our operating system, check whether it is properly working by typing the following command in CLI / Terminal.


python --version

Output:


Python 3.9.13

Other Python installation methods

We can also install Python using various CLI tools or through the Windows store.

You can check out our full guide on how to install Python for more details. Similarly, our interactive Introduction to Python course helps you master the basics of Python syntax, lists, functions, packages, and Numpy.

Installing VSCode

Installing VSCode is super simple. Download and install the stable build from the official website. The installer is available for all kinds of operating systems, including web browsers.

Image from Visual Studio Code

Other VSCode installation methods

We can install using Microsoft store, Snap Store, and multiple CLI tools for Windows, Linux, and macOS.

Running Python in VSCode

After installing Python and VSCode, it is time to write a simple code and run the Python file within the IDE.

Create a new file

At start, you will see the welcome note. Ignore that and go to File > New Text File or use the keyboard shortcut Ctrl + N to create a new file. After that, write a simple print expression to display “Hello World.”

Save Python file

Save the file using Ctrl + S. Select the file directory and type the file name. Make sure to add `.py` at the end of the file name.

Select the interpreter

To run the Python file, we need to select the Python interpreter. By default, the Anaconda environment comes with Python version 3.9.13.

Run a Python file

To run the Python file, simply click on the Run button on the top left, as shown in the image. It will initialize the terminal and run the Python file to display the output.

You can also type python test.py in the terminal to run the file present in the current directory.

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.

How To Create & Activate A Virtual Environment In Visual Studio Code (Python 3.12)
How To Create & Activate A Virtual Environment In Visual Studio Code (Python 3.12)

Next steps

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

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

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.

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

I am using VSCode with ArcGIS Pro 3.0 in a virtual environment. Until yesterday, everything worked just fine. After updating to Pro 3.0, I was still able to use open a script and then have it run in the terminal window.

Previously, I was able to select a line from the script, run it, and then it would open the correct interpreter. However, now I am unable to do so and cannot troubleshoot why this is happening. I have added the correct path to the ArcGIS Pro python executable in the interpreter path, but the terminal opens to another python executable. Any advice would be greatly appreciated as to how I can run specific python executable that I want to run.

UPDATE: I can open VSCode using code from my anaconda installation, but still am having trouble running python interactively in the terminal. Previously, I used to be able to do this (e.g. test indented code cells), but this doesn’t seem to be functioning anymore.

Extension Marketplace

Increase the power of Visual Studio Code through Extensions

The features that Visual Studio Code includes out-of-the-box are just the start. VS Code extensions let you add languages, debuggers, and tools to your installation to support your development workflow. VS Code’s rich extensibility model lets extension authors plug directly into the VS Code UI and contribute functionality through the same APIs used by VS Code. This article explains how to find, install, and manage VS Code extensions from the Visual Studio Code Marketplace.

Best Python IDE: Vim, Emacs, PyCharm, or Visual Studio Code? | Guido van Rossum and Lex Fridman
Best Python IDE: Vim, Emacs, PyCharm, or Visual Studio Code? | Guido van Rossum and Lex Fridman

Creating environments

Using the Create Environment command

To create local environments in VS Code using virtual environments or Anaconda, you can follow these steps: open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), search for the Python: Create Environment command, and select it.

The command presents a list of environment types: Venv or Conda.

If you are creating an environment using Venv, the command presents a list of interpreters that can be used as a base for the new virtual environment.

If you are creating an environment using Conda, the command presents a list of Python versions that can be used for your project.

After selecting the desired interpreter or Python version, a notification will show the progress of the environment creation and the environment folder will appear in your workspace.

Note: The command will also install necessary packages outlined in a requirements/dependencies file, such as


requirements.txt

,

pyproject.toml

, or

environment.yml

, located in the project folder. It will also add a

.gitignore

file to the virtual environment to help prevent you from accidentally committing the virtual environment to source control.

Create a virtual environment in the terminal

If you choose to create a virtual environment manually, use the following command (where “.venv” is the name of the environment folder):


# macOS/Linux # You may need to run `sudo apt-get install python3-venv` first on Debian-based OSs python3 -m venv .venv # Windows # You can also use `py -3 -m venv .venv` python -m venv .venv

Note: To learn more about the


venv

module, read Creation of virtual environments on Python.org.

When you create a new virtual environment, a prompt will be displayed in VS Code to allow you to select it for the workspace.

Tip: Make sure to update your source control settings to prevent accidentally committing your virtual environment (in for example


.gitignore

). Since virtual environments are not portable, it typically does not make sense to commit them for others to use.

Create a conda environment in the terminal

The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment named

env-01

with a Python 3.9 interpreter and several libraries:


conda create -n env-01 python=3.9 scipy=0.15.0 numpy

Note: For more information on the conda command line, you can read Conda environments.

Additional notes:

  • If you create a new conda environment while VS Code is running, use the refresh icon on the top right of the Python: Select Interpreter window; otherwise you may not find the environment there.

  • To ensure the environment is properly set up from a shell perspective, use an Anaconda prompt and activate the desired environment. Then, you can launch VS Code by entering the


    code .

    command. Once VS Code is open, you can select the interpreter either by using the Command Palette or by clicking on the status bar.

  • Although the Python extension for VS Code doesn’t currently have direct integration with conda


    environment.yml

    files, VS Code itself is a great YAML editor.

  • Conda environments can’t be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal – Terminal profiles.

  • You can manually specify the path to the


    conda

    executable to use for activation (version 4.4+). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and run Preferences: Open User Settings. Then set

    python.condaPath

    , which is in the Python extension section of User Settings, with the appropriate path.

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.

PyScript | Run Python in your HTML | Write your first PyScript Program in VSCode | PYTHON in BROWSER
PyScript | Run Python in your HTML | Write your first PyScript Program in VSCode | PYTHON in BROWSER

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.

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!

Top 7 Best Extensions In VSCode For Python Programming That I Use For Data Science Projects
Top 7 Best Extensions In VSCode For Python Programming That I Use For Data Science Projects

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).

Conclusion

Like I said, these extensions and my personal configuration are not a silver bullet. But this setup is something that I’ve been using for quite a while and I hope it’s useful to you as well.

I often install specialized extensions depending on the projects I work on. For example, I use the Django or Jinja project when I work on a Django or Flask project.

Or I install the Jupyter extension while working on a Jupyter Notebook. So feel free to install whatever you need, just don’t overdo it.

Summary: in this tutorial, you’ll learn how to set up Visual Studio Code for Python development.

Getting Started with Python in Visual Studio Code | Python with VSCode
Getting Started with Python in Visual Studio Code | Python with VSCode

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.

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.

My Python Development Environment Setup - Full Tutorial
My Python Development Environment Setup – Full Tutorial

Installing and Configuring Visual Studio Code for Python Development

Installing Visual Studio Code is very accessible on any platform. Full instructions for Windows, Mac, and Linux are available, and the editor is updated monthly with new features and bug fixes. You can find everything at the Visual Studio Code website:

In case you were wondering, Visual Studio Code (or VS Code for short) shares almost nothing other than a name with its larger Windows-based namesake, Visual Studio.

Note: To learn how to set up VS Code as part of a full Python coding environment on a Windows machine, check out this comprehensive guide.

Visual Studio Code has built-in support for multiple languages and an extension model with a rich ecosystem of support for others. VS Code is updated monthly, and you can keep up to date at the Microsoft Python blog. Microsoft even makes the VS Code GitHub repo available for anyone to clone and contribute. (Cue the PR flood.)

The VS Code UI is well documented, so I won’t rehash it here:

Extensions for Python Development

As stated above, VS Code supports development in multiple programming languages through a well-documented extension model. The Python extension enables Python development in Visual Studio Code, with the following features:

  • Support for Python 3.4 and higher, as well as Python 2.7
  • Code completion with IntelliSense
  • Linting
  • Debugging support
  • Code snippets
  • Unit testing support
  • Automatic use of conda and virtual environments
  • Code editing in Jupyter environments and Jupyter Notebooks

Visual Studio Code extensions cover more than just programming language capabilities:

  • Keymaps allow users already familiar with Atom, Sublime Text, Emacs, Vim, PyCharm, or other environments to feel at home.

  • Themes customize the UI whether you like coding in the light, dark, or something more colorful.

  • Language packs provide a localized experience.

Here are some other extensions and settings I find useful:

  • GitLens provides tons of useful Git features directly in your editing window, including blame annotations and repository exploration features.

  • Auto save is easily turned on by selecting


    File, Auto Save

    from the menu. The default delay time is 1000 milliseconds, which is also configurable.

  • Settings Sync allows you to synchronize your VS Code settings across different installations using GitHub. If you work on different machines, this helps keep your environment consistent across them.

  • Docker lets you quickly and easily work with Docker, helping author


    Dockerfile

    and

    docker-compose.yml

    , package and deploy your projects, and even generate the proper Docker files for your project.

Of course, you may discover other useful extensions as you use VS Code. Please share your discoveries and settings in the comments!

Discovering and installing new extensions and themes is accessible by clicking on the Extensions icon on the Activity Bar. You can search for extensions using keywords, sort the results numerous ways, and install extensions quickly and easily. For this article, install the Python extension by typing

python

in the Extensions item on the Activity Bar, and clicking Install:

You can find and install any of the extensions mentioned above in the same manner.

Visual Studio Code Configuration Files

One important thing to mention is that Visual Studio Code is highly configurable through user and workspace settings.

User settings are global across all Visual Studio Code instances, while workspace settings are local to the specific folder or project workspace. Workspace settings give VS Code tons of flexibility, and I call out workspace settings throughout this article. Workspace settings are stored as

.json

files in a folder local to the project workspace called

.vscode

.

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.

HOW TO DOWNLOAD & INSTALL ANY PYTHON VERSION EASILY ON PC ( 2024 GUIDE )
HOW TO DOWNLOAD & INSTALL ANY PYTHON VERSION EASILY ON PC ( 2024 GUIDE )

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.

Install from a VSIX

You can manually install a VS Code extension packaged in a

.vsix

file. Using the Install from VSIX command in the Extensions view command dropdown, or the Extensions: Install from VSIX command in the Command Palette, point to the

.vsix

file.

You can also install using the VS Code

--install-extension

command-line switch providing the path to the

.vsix

file.


code --install-extension myextension.vsix

You may provide the

--install-extension

multiple times on the command line to install multiple extensions at once.

If you’d like to learn more about packaging and publishing extensions, see our Publishing Extensions article in the Extension API.

Python Tutorial for Beginners with VS Code 🐍
Python Tutorial for Beginners with VS Code 🐍

Install an extension

To install an extension, select the Install button. Once the installation is complete, the Install button will change to the Manage gear button.

Find and install an extension

For example, let’s install the popular TODO Highlight extension. This extension highlights text like ‘TODO:’ and ‘FIXME:’ in your source code so you can quickly find undone sections.

In the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)), type ‘todo’ in the search box to filter the Marketplace offerings to extensions with ‘todo’ in the title or metadata. You should see the TODO Highlight extension in the list.

An extension is uniquely identified by its publisher and extension IDs. If you select the TODO Highlight extension, you will see the Extension details page, where you can find the extension ID, in this case,

wayou.vscode-todo-highlight

. Knowing the extension ID can be helpful if there are several similarly named extensions.

Select the Install button, and VS Code will download and install the extension from the Marketplace. When the installation is complete, the Install button will be replaced with a Manage gear button.

To see the TODO Highlight extension in action, open any source code file and add the text ‘TODO:’ and you will see the text highlighted.

The TODO Highlight extension contributes the commands, TODO-Highlight: List highlighted annotations and TODO-Highlight: Toggle highlight, that you can find in the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)). The TODO-Highlight: Toggle highlight command lets you quickly disable or enable highlighting.

The extension also provides settings for tuning its behavior, which you can find in the Settings editor (⌘, (Windows, Linux Ctrl+,)). For example, you might want the text search to be case insensitive and you can uncheck the Todohighlight: Is Case Sensitive setting.

If an extension doesn’t provide the functionality you want, you can always Uninstall the extension from the Manage button context menu.

This has been just one example of how to install and use an extension. The VS Code Marketplace has thousands of extensions supporting hundreds of programming languages and tasks. Everything from full featured language support for Java, Python, Go, and C++ to simple extensions that create GUIDs, change the color theme, or add virtual pets to the editor.

Extension details

On the extension details page, you can read the extension’s README and review the extension’s:

  • Feature Contributions – The extension’s additions to VS Code such as settings, commands and keyboard shortcuts, language grammars, debugger, etc.
  • Changelog – The extension repository CHANGELOG if available.
  • Dependencies – Lists if the extension depends on any other extensions.

If an extension is an Extension Pack, the Extension Pack section will display which extensions will be installed when you install the pack. Extension Packs bundle separate extensions together so they can be easily installed at one time.

Extensions view filter and commands

You can filter the Extensions view with the Filter Extensions context menu.

There are filters to show:

  • The list of outdated extensions that can be updated
  • The list of currently enabled/disabled extensions
  • The list of recommended extensions based on your workspace
  • The list of globally popular extensions

You can sort the extension list by Install Count, Rating, Name, Published Date, or Updated Date in either ascending or descending order. You can learn more about extension search filters below.

You can run additional Extensions view commands via the

...

View and More Actions button.

Through this context menu you can control extension updates, enable or disable all extensions, and use the Extension Bisect utility to isolate problematic extension behavior.

Search for an extension

You can clear the Search box at the top of the Extensions view and type in the name of the extension, tool, or programming language you’re looking for.

For example, typing ‘python’ will bring up a list of Python language extensions:

If you know the exact identifier for an extension you’re looking for, you can use the

@id:

prefix, for example

@id:vue.volar

. Additionally, to filter or sort results, you can use the filter and sort commands, detailed 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.

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.

How to Run Python in Visual Studio Code on Windows 10 [2022] | Run Sample Python Program
How to Run Python in Visual Studio Code on Windows 10 [2022] | Run Sample Python Program

Debugging and Testing in VSCode

Debugging

The Python extension comes with Debugging for all kinds of applications like multi-threaded, web, and remote applications. We can set breakpoints, inspect data, and run programs step by step.

Select a debug configuration

Launch the debug tab by clicking on the debug icon on the action bar or by using the keyboard shortcut Ctrl + Shift +D. To customize Debug options, click on create a launch.json file and select Python File.

Debug Panel

Run the Debug by clicking on the blue button Run and Debug, and it will run the Python file and show us the Variables, Watch, Call Stack, and breakpoints.

Quick debug

For quick debugging, you can always click on the down arrow beside the Run button and select Debug Python File.

Testing

The Python extension supports unittest and pytest testing frameworks. Instead of reading the test results in a terminal, you can review and resolve the issues within the Testing tab in an active bar.

Configure Python tests

After clicking on the Testing button, we will click on the Configure Python Tests button and select the testing framework. Usually, VSCode automatically detects the framework and displays all the tests in a tree view.

Learn about Python unit testing, implementing Python’s pytest testing framework by following our how to use pytest for unit testing tutorial.

Note: The testing example that we are using is from Visual Studio Code official documentation.

Run the unittest

We can run the Unit test by clicking on the Run Test button in the Testing tab and analyzing the results.

As we can observe, 1 of 2 tests have passed, and it has displayed the reasoning behind the failed result. VSCode testing is highly interactive and user-friendly.

Command line extension management

To make it easier to automate and configure VS Code, it is possible to list, install, and uninstall extensions from the command line. When identifying an extension, provide the full name of the form

publisher.extension

, for example

ms-python.python

.

Example:


code --extensions-dir


Set the root path for extensions. code --list-extensions List the installed extensions. code --show-versions Show versions of installed extensions, when using --list-extension. code --install-extension (

|

) Installs an extension. code --uninstall-extension (

|

) Uninstalls an extension. code --enable-proposed-api (

) Enables proposed API features for extensions. Can receive one or more extension IDs to enable individually.






You can see the extension ID on the extension details page under the Marketplace Info.

How to setup Python for VSCode in 3 mins only!! I Install Python and Setup VSCode for Windows 10!
How to setup Python for VSCode in 3 mins only!! I Install Python and Setup VSCode for Windows 10!

Code Actions

Code Actions (also known as Quick Fixes) are provided to help fix issues when there are warnings in your code. These helpful hints are displayed in the editor left margin as a lightbulb (💡). Select the light bulb to display Code Action options. These Code Action can come from extensions such as Python, Pylance, or VS Code itself. For more information about Code Actions, see Python Quick Fixes.

Tips and Tricks for Efficient Python Development in VSCode

VSCode comes with awesome Python development features and extensions. We can customize them to our needs and improve the productivity. In this section, we will learn about tips and tricks for efficient Python development.

  1. Getting started: Help > Get Started. Learn about VSCode’s customization and features by following guided tutorials.
  2. Command Palette: access entire all available commands by using the Keyboard shortcut: Ctrl+Shift+P. By writing keywords, we can access specific commands.
  3. Keyboard shortcuts: better than command palettes. We can modify keyboard shortcuts or memorize them by using keyboard reference sheets. It will help us access the commands directly, instead of searching with the keyword.
  4. Command line: launch the VSCode editor through the command line interface by typing `code .`. We can also customize how the editor is launched by adding additional arguments.
  5. Errors and warnings: quick jump to errors and warnings in a project by using the keyboard shortcut: Ctrl+Shift+M. We can also cycle through the error with F8 or Shift+F8.
  6. Customization: VSCode allows us to customize themes, keyboard shortcuts, JSON validation, debugging settings, fonts, and many more. It is a fully customizable IDE.
  7. Extensions: other Python extensions improve our development experience. Look for popular extensions on the Visual Studio Marketplace.
  8. Multi cursor selection: is a lifesaver. Add cursors at arbitrary positions by using Alt+Click. It will allow us to modify multiple lines of code at once. We can also use Ctrl+Shift+L to modify all occurrences of the current selection.
  9. Search and modify: this is the best tool for searching and modifying multiple expressions at once. We can also rename the symbol by selecting the symbol and typing F2.
  10. Git integration: allows us to perform all Git-related tasks within IDE. It provides an easy-to-use GUI for diff, views, staging, branching, committing, merging, and more.
  11. Code Snippets: is our best friend. Just like Autohotkey, we are creating templates for repeating code patterns. To create a custom code snippet, select File > Preferences > Configure User Snippets and then select the language.
  12. GitHub Copilot: is a winner extension for all kinds of development. It enhances the coding experience with artificial intelligence (AI) by suggesting lines of code or entire functions.

Bonus: sync your settings by logging into your GitHub account. It will sync your settings across all of the machines.

How to run Python in Visual Studio Code on Windows 10/11 [ 2024 Update ] Python Developers
How to run Python in Visual Studio Code on Windows 10/11 [ 2024 Update ] Python Developers

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.

Installing Essential VSCode Python Extensions

The VSCode’s Python extensions will provide us with the helping functionalities for code editing, docstrings, linting, formatting, debugging, testing, and environment selection.

How to install a VSCode Extension

Click on the box icon on the activity bar or use a keyboard shortcut: Ctrl + Shift + X to open the extension panel. Type any keyword in the search bar to explore all kinds of extensions.

Install VSCode Python extension

In our case, we will type Python and install the Python extension by clicking on the install button, as shown above.

List of Essential Python Extensions

Python

The Python extension automatically installs Pylance, Jupyter, and isort extensions. It comes with a complete collection of tools for Data Science, web development, and software engineering.

Key Features:

Python extension comes with IntelliSense, linting, debugging, code navigation, code formatting, refactoring, variable explorer, and test explorer.

  • IntelliSense (code autocomplete)
  • Linting (Pylint, Flake8)
  • Code formatting (black, autopep)
  • Debugging
  • Testing (unittest, pytest)
  • Jupyter Notebooks
  • Environments (venv, pipenv, conda)
  • Refactoring
Indent-rainbow

Indent-rainbow extensions provide us with a multilevel colorized indentation for improved code readability. We get alternating colors on each step, and it helps us avoid common indentation errors.

Python Indent

Python Indent extension helps us with creating indentations. By pressing the Enter key, the extension will parse the Python file and determine how the next line should be indented. It is a time saver.

Jupyter Notebook Renderers

Jupyter Notebook Renderers is part of the Jupyter extension pack. It helps us render plotly, vega, gif, png, svg, and jpeg output.

autoDocstring

The autoDocstring extension helps us quickly generate docstring for Python functions. By typing triple quotes “”” or ”’ within the function, we can generate and modify docstring. Learn more about doc strings by following our Python Docstrings tutorial.

Note: Most Python development extensions and features come with Python extensions.

HƯỚNG DẪN CÀI ĐẶT PYTHON & VISUAL STUDIO CODE CHI TIẾT!
HƯỚNG DẪN CÀI ĐẶT PYTHON & VISUAL STUDIO CODE CHI TIẾT!

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.

Configure and run the debugger

Let’s now try debugging our Python program. Debugging support is provided by the Python Debugger extension, which is automatically installed with the Python extension. To ensure it has been installed correctly, open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)) and search for

@installed python debugger

. You should see the Python Debugger extension listed in the results.

Next, set a breakpoint on line 2 of

hello.py

by placing the cursor on the

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.

The debugger will start, and then 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 can see that the

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

Setup Python Using Visual Studio Code On Mac | VSCode Python | Python Mac | Python 3
Setup Python Using Visual Studio Code On Mac | VSCode Python | Python Mac | Python 3

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.

Manage extensions

VS Code makes it easy to manage your extensions. You can install, disable, update, and uninstall extensions through the Extensions view, the Command Palette (commands have the Extensions: prefix) or command-line switches.

List installed extensions

By default, the Extensions view will show the extensions you currently have installed, and all extensions that are recommended for you. You can use the Extensions: Focus on Installed View command, available in the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) or in the More Actions (

...

) dropdown menu > Views > Installed, to clear any text in the search box and show the list of all installed extensions, which includes those that have been disabled.

Uninstall an extension

To uninstall an extension, select the Manage gear button at the right of an extension entry and then choose Uninstall from the dropdown menu. This will uninstall the extension and prompt you to reload VS Code.

Disable an extension

If you don’t want to permanently remove an extension, you can instead temporarily disable the extension by clicking the gear button at the right of an extension entry. You can disable an extension globally or just for your current Workspace. You will be prompted to reload VS Code after you disable an extension.

If you want to quickly disable all installed extensions, there is a Disable All Installed Extensions command in the Command Palette and More Actions (

...

) dropdown menu.

Extensions remain disabled for all VS Code sessions until you re-enable them.

Enable an extension

Similarly if you have disabled an extension (it will be in the Disabled section of the list and marked Disabled), you can re-enable it with the Enable or Enable (Workspace) commands in the dropdown menu.

There is also an Enable All Extensions command in the More Actions (

...

) dropdown menu.

Extension auto-update

VS Code checks for extension updates and installs them automatically. After an update, you will be prompted to reload VS Code. If you’d rather update your extensions manually, you can disable auto-update with the Disable Auto Updating Extensions command that sets the

extensions.autoUpdate

setting to

false

.

If you don’t want VS Code to even check for updates, you can set the

extensions.autoCheckUpdates

setting to false.

Update an extension manually

If you have extensions auto-update disabled, you can quickly look for extension updates by using the Show Outdated Extensions command that uses the

@updates

filter. This will display any available updates for your currently installed extensions.

Select the Update button for the outdated extension. The update will be installed, and you’ll be prompted to reload VS Code. You can also update all your outdated extensions at one time with the Update All Extensions command.

If you also have automatic checking for updates disabled, you can use the Check for Extension Updates command to check which of your extensions can be updated.

Visual Studio Code (Windows) - Setting up a Python Development Environment and Complete Overview
Visual Studio Code (Windows) – Setting up a Python Development Environment and Complete Overview

Workspace recommended extensions

A good set of extensions can make working with a particular workspace or programming language more productive and you’d often like to share this list with your team or colleagues. You can create a recommended list of extensions for a workspace with the Extensions: Configure Recommended Extensions (Workspace Folder) command.

In a single folder workspace, the command creates an

extensions.json

file located in the workspace

.vscode

folder where you can add a list of extensions identifiers ({publisherName}.{extensionName}).

In a multi-root workspace, the command will open your

.code-workspace

file where you can list extensions under

extensions.recommendations

. You can still add extension recommendations to individual folders in a multi-root workspace by using the Extensions: Configure Recommended Extensions (Workspace Folder) command.

An example

extensions.json

could be:


{ "recommendations": ["dbaeumer.vscode-eslint", "esbenp.prettier-vscode"] }

which recommends a linter extension and a code formatter extension.

An extension is identified using its publisher name and extension identifier

publisher.extension

. You can see the name on the extension’s detail page. VS Code will provide you with auto-completion for installed extensions inside these files.

VS Code prompts a user to install the recommended extensions when a workspace is opened for the first time. The user can also review the list with the Extensions: Show Recommended Extensions command.

How to create and open a Python project or file

If you have an existing Python project you wish to work on in VS Code, you can begin by opening your folder or file from the VS Code Welcome page or File Explorer view, or by selecting File > Open Folder (Ctrl+K Ctrl+O) or File > Open File (⌘O (Windows, Linux Ctrl+O)).

You can create a new Python file by selecting New File on the VS Code Welcome page and then selecting Python file, or by navigating to File > New File ().

Tip: If you already have a workspace folder open in VS Code, you can add new files or folders directly into your existing project. You can create new folders and files by using the corresponding New Folder or New File icons on the top level folder in the File Explorer view.

Setting Up VSCode For Python Programming
Setting Up VSCode For Python Programming

Recommended extensions

You can see a list of recommended extensions using Show Recommended Extensions, which sets the

@recommended

filter. Extension recommendations can either be:

  • Workspace Recommendations – Recommended by other users of your current workspace.
  • Other Recommendations – Recommended based on recently opened files.

See the section below to learn how to contribute recommendations for other users in your project.

Ignoring recommendations

To dismiss a recommendation, select on the extension item to open the Details page and then select the Manage gear button to display the context menu. Select the Ignore Recommendation menu item. Ignored recommendations will no longer be recommended to you.

Next steps

  • Editing code – Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
  • 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.
How to Setup Python in Visual Studio Code on Windows 11
How to Setup Python in Visual Studio Code on Windows 11

Editing an Existing Python Project

In the Sieve of Eratosthenes example, you created a single Python file. That’s great as an example, but many times, you’ll create larger projects and work on them over a longer period of time. A typical new project work flow might look like this:

  • Create a folder to hold the project (which may include a new GitHub project)
  • Change to the new folder
  • Create the initial Python code using the command

    code filename.py

Using Visual Studio Code on a Python project (as opposed to a single Python file) opens up tons more functionality that lets VS Code truly shine. Let’s take a look at how it works with a larger project.

Late in the previous millennium, when I was a much younger programmer, I wrote a calculator program that parsed equations written in infix notation, using an adaptation of Edsger Dijkstra’s shunting yard algorithm.

To demonstrate the project-focused features of Visual Studio Code, I began recreating the shunting yard algorithm as an equation evaluation library in Python. To continue following along, feel free to clone the repo locally.

Once the folder is created locally, you can open the entire folder in VS Code quickly. My preferred method (as mentioned above) is modified as follows, since I already have the folder and basic files created:


cd /path/to/project code .

VS Code understands, and will use, any virtualenv, pipenv, or conda environments it sees when opened this way. You don’t even need to start the virtual environment first! You can even open a folder from the UI, using File, Open Folder from the menu, Ctrl+K, Ctrl+O from the keyboard, or File:Open Folder from the Command Palette.

For my equation eval library project, here’s what I see:

When Visual Studio Code opens the folder, it also opens the files you last had opened. (This is configurable.) You can open, edit, run, and debug any file listed. The Explorer view in the Activity Bar on the left gives you a view of all the files in the folder and shows how many unsaved files exist in the current set of tabs.

Configuring Linting and Formatting in VSCode

Linting

Linting highlights the problems in the Python source code and provides us with suggestions. It generally highlights syntactical and stylistic issues. Linting helps you identify and correct coding issues that can lead to errors.

You can select the linting method by selecting Python: Select Linter command in the command palette (Ctrl+Shift+P). You can also manually enable the linting method in settings.

Select linting method

In our case, we have selected the flake8 method. You can also review the list of available linting methods.

  • Enable/ Disable Linting: select Python: Enable/Disable Linting in command palette.
  • Run Linting: command palette (Ctrl+Shift+P) > Python: Run Linting.

Fixing the error

After running the Python linter, you will see the issues with the suggestions.

Note: Enabling a different linter will prompt you to install the required Python package.

Formatting

Formatting makes code readable. It follows specific rules for line spacing, indents, spacing around operators, and closing brackets. The Python extension supports three Python formatting methods: autopep8, black, or yapf.

By reading about PEP-8: Python Naming Conventions & Code Standards, you can learn Python’s style guide and formatting rules.

Select the Python formatter

To access the formatting option, we have to open the settings panel by going to Preferences -> Settings or using the keyboard shortcut: Ctrl +,. After that, type “python formatting provider” in the search bar and select “black” from the dropdown menu.

Configure Python formatter

For formatting the Python file on save, we have to search for format on save in the Settings and enable the Editor: Format on Save option.

LIVE 🔴: Setting up VS Code for Python Beginners 🐍
LIVE 🔴: Setting up VS Code for Python Beginners 🐍

Debugging Support

Even though VS Code is a code editor, debugging Python directly within VS Code is possible. VS Code offers many of the features you would expect from a good code debugger, including:

  • Automatic variable tracking
  • Watch expressions
  • Breakpoints
  • Call stack inspection

You can see them all as part of the Debug view on the Activity Bar:

The debugger can control Python apps running in the built-in terminal or an external terminal instance. It can attach to an already running Python instances, and can even debug Django and Flask apps.

Debugging code in a single Python file is as simple as starting the debugger using F5. You use F10 and F11 to step over and into functions respectively, and Shift+F5 to exit the debugger. Breakpoints are set using F9, or using the mouse by clicking in the left margin in the editor window.

Before you start debugging more complicated projects, including Django or Flask applications, you need to setup and then select a debug configuration. Setting up the debug configuration is relatively straightforward. From the Debug view, select the Configuration drop-down, then Add Configuration, and select Python:

Visual Studio Code will create a debug configuration file under the current folder called

.vscode/launch.json

, which allows you to setup specific Python configurations as well as settings for debugging specific apps, like Django and Flask.

You can even perform remote debugging, and debug Jinja and Django templates. Close the

launch.json

file in the editor and select the proper configuration for your application from the Configuration drop-down.

More Python resources

  • Getting Started with Python in VS Code – Learn how to edit, run, and debug code in VS Code.
  • Virtual Environments and Packages (Python.org) – Learn more about virtual environments and packages.
  • Installing Python Modules (Python.org) – Learn how to install Python modules.
  • Python tutorial (Python.org) – Learn more about the Python language.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Development in Visual Studio Code (Setup Guide)

One of the coolest code editors available to programmers, Visual Studio Code, is an open-source, extensible, light-weight editor available on all platforms. It’s these qualities that make Visual Studio Code from Microsoft very popular, and a great platform for Python development.

In this article, you’ll learn about Python development in Visual Studio Code, including how to:

  • Install Visual Studio Code
  • Discover and install extensions that make Python development easy
  • Write a straightforward Python application
  • Learn how to run and debug existing Python programs in VS Code
  • Connect Visual Studio Code to Git and GitHub to share your code with the world

We assume you are familiar with Python development and already have some form of Python installed on your system (Python 2.7, Python 3.6/3.7, Anaconda, or others). Screenshots and demos for Ubuntu and Windows are provided. Because Visual Studio Code runs on all major platforms, you may see slightly different UI elements and may need to modify certain commands.

If you already have a basic VS Code setup and you’re hoping to dig deeper than the goals in this tutorial, you might want to explore some advanced features in VS Code.

Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level.

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.

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!

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.

Python Extension

The first extension that you need to install is the Python Extension from Microsoft.

This is actually an extension pack that contains two extensions. The first extension is the Python extension. It lays the foundation for Python development in Visual Studio Code.

The other one is Pylance, which is a very performant language server for Python.

This extension provides rich intellisense support and is powered by Pyright, the static type checker from Microsoft. The next thing you need to think about is linting.

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

Browse for extensions

You can browse and install extensions from within VS Code. Bring up the Extensions view by clicking on the Extensions icon in the Activity Bar on the side of VS Code or the View: Extensions command (⇧⌘X (Windows, Linux Ctrl+Shift+X)).

This will show you a list of the most popular VS Code extensions on the VS Code Marketplace.

Each extension in the list includes a brief description, the publisher, the download count, and a five star rating. You can select the extension item to display the extension’s details page where you can learn more.

Note: If your computer’s Internet access goes through a proxy server, you will need to configure the proxy server. See Proxy server support for details.

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

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.

Isort

Like a linter, isort is another utility that’s sole purpose is sorting import statements.

The utility sorts all the imports alphabetically, while also dividing them into sections.

The extension is very straightforward. Once you have the extension, it’ll render squiggly lines under any import statement that seems out of place.

You can then use the quick action menu to sort them. Or, you can also use the command palette to quickly access the isort command.

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.

Error Lens

While not related to Python specifically, Error Lens is a great extension that embeds errors right by the side of the line of code.

I often work on my 14 inch Thinkpad and like to turn off the terminal pane. Error Lens eradicates the need to look at the terminal now and then to see my errors and warnings.

As useful as it may be, sometimes your editor can look cluttered due to all the warning and error outputs, so decide accordingly.

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!

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!

Common questions

Where are extensions installed?

Extensions are installed in a per user extensions folder. Depending on your platform, the location is in the following folder:

  • Windows

    %USERPROFILE%\.vscode\extensions
  • macOS

    ~/.vscode/extensions
  • Linux

    ~/.vscode/extensions

You can change the location by launching VS Code with the

--extensions-dir

command-line option.

Whenever I try to install any extension, I get a connect ETIMEDOUT error

You may see this error if your machine is going through a proxy server to access the Internet. See the Proxy server support section in the setup topic for details.

Can I download an extension directly from the Marketplace?

Some users prefer to download an extension once from the Marketplace and then install it multiple times from a local share. This is useful when there are connectivity concerns or if your development team wants to use a fixed set of extensions.

To download an extension, navigate to the details page for the specific extension within the Marketplace. On that page, there is a Download Extension link in the Resources section, which is located on the right-hand side of the page.

Once downloaded, you can then install the extension via the Install from VSIX command in the Extensions view command dropdown.

Can I stop VS Code from providing extension recommendations?

Yes, if you would prefer to not have VS Code display extension recommendations in the Extensions view or through notifications, you can modify the following settings:


  • extensions.showRecommendationsOnlyOnDemand

    – Set to true to remove the RECOMMENDED section.

  • extensions.ignoreRecommendations

    – Set to true to silence extension recommendation notifications.

The Show Recommended Extensions command is always available if you want to see recommendations.

Can I trust extensions from the Marketplace?

The Marketplace runs a virus scan on each extension package that’s published to ensure its safety. The virus scan is run for each new extension and for each extension update. Until the scan is all clear, the extension won’t be published in the Marketplace for public usage.

The Marketplace also prevents extension authors from name-squatting on official publishers such as Microsoft and RedHat.

If a malicious extension is reported and verified, or a vulnerability is found in an extension dependency:

  1. The extension is removed from the Marketplace.
  2. The extension is added to a kill list so that if it has been installed, it will be automatically uninstalled by VS Code.

The Marketplace also provides you with resources to make an informed decision about the extensions you install:

  • Ratings & Review – Read what others think about the extension.
  • Q & A – Review existing questions and the level of the publisher’s responsiveness. You can also engage with the extension’s publisher(s) if you have concerns.
  • Issues, Repository, and License – Check if the publisher has provided these and if they have the support you expect.

If you do see an extension that looks suspicious, you can report the extension to the Marketplace with the Report Abuse link at the bottom of the extension More Info section.

Python environments in VS Code

An “environment” in Python is the context in which a Python program runs that consists of an interpreter and any number of installed packages.

Note: If you’d like to become more familiar with the Python programming language, review More Python resources.

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

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.

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).

Git Integration

VSCode comes with built-in Git integration. No more writing Git commands on terminals. Git integration provides a user-friendly GUI and helpful functions for diff, views, staging, branching, committing, merge, and more.

Check out our Git Cheat Sheet to learn about the various Git commands and functionalities.

Note: To enable Git integration, you need to install Git from official site.

Initializing Git

We can access it through the action bar or by using the keyboard shortcut: Ctrl + Shift + G. Before we start committing, we need to initialize the repository.

Git Commit

After that, add and commit the changes with the message. It is that simple.

Create a GitHub repository and push the code

You can even create a GitHub repository and push your code to a remote server by logging into your GitHub account.

Private GitHub repository

We have created a GitHub private repository of Python files and folders.

You can now simply commit and push the changes to the remote server without leaving the VSCode.

Follow our Github and Git tutorial to learn everything about Git and GitHub.

Types of Python environments

Global environments

By default, any Python interpreter installed runs in its own global environment. For example, if you just run

python

,

python3

, or

py

at a new terminal (depending on how you installed Python), you’re running in that interpreter’s global environment. Any packages that you install or uninstall affect the global environment and all programs that you run within it.

Tip: In Python, it is best practice to create a workspace-specific environment, for example, by using a local environment.

Local environments

There are two types of environments that you can create for your workspace: virtual and conda. These environments allow you to install packages without affecting other environments, isolating your workspace’s package installations.

Virtual environments

A virtual environment is a built-in way to create an environment. A virtual environment creates a folder that contains a copy (or symlink) to a specific interpreter. When you install packages into a virtual environment it will end up in this new folder, and thus isolated from other packages used by other workspaces.

Note: While it’s possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension.

Conda environments

A conda environment is a Python environment that’s managed using the

conda

package manager (see Getting started with conda).Choosing between conda and virtual environments depends on your packaging needs, team standards, etc.

Python environment tools

The following table lists the various tools involved with Python environments:

Tool Definition and Purpose
pip The Python package manager that installs and updates packages. It’s installed with Python 3.9+ by default (unless you are on a Debian-based OS; install
venv Allows you to manage separate package installations for different projects and is installed with Python 3 by default (unless you are on a Debian-based OS; install
conda Installed with Miniconda. It can be used to manage both packages and virtual environments. Generally used for data science projects.

Run, debug, and test

Now that you are more familiar with Python in VS Code, let’s learn how to run, debug, and test your code.

Run

There are a few ways to run Python code in VS Code.

To run the Python script you have open on the editor, select the Run Python File in Terminal play button in the top-right of the editor.

There are also additional ways you can iteratively run snippets of your Python code within VS Code:

  • 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.

Debug

The debugger is a helpful tool that allows you to inspect the flow of your code execution and more easily identify errors, as well as explore how your variables and data change as your program is run. You can start debugging by setting a breakpoint in your Python project by clicking in the gutter next to the line you wish to inspect.

To start debugging, initialize the debugger by pressing F5. Since this is your first time debugging this file, a configuration menu will open allowing you to select the type of application you want to debug. If it’s a Python script, you can select Python File.

Once your program reaches the breakpoint, it will stop and allow you to track data in the Python Debug console, and progress through your program using the debug toolbar.

For a deeper dive into Python debugging functionality, see Python debugging in VS Code.

Test

The Python extension provides robust testing support for Unittest and pytest.

You can configure Python tests through the Testing view on the Activity Bar by selecting Configure Python Tests and selecting your test framework of choice.

You can also create tests for your Python project, which the Python extension will attempt to discover once your framework of choice is configured. The Python extension also allows you to run and debug your tests in the Testing view and inspect the test run output in the Test Results panel.

For a comprehensive look at testing functionality, see Python testing in VS Code.

Next steps

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

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

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.

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

Configure and run the debugger

Let’s now try debugging our Python program. Debugging support is provided by the Python Debugger extension, which is automatically installed with the Python extension. To ensure it has been installed correctly, open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)) and search for

@installed python debugger

. You should see the Python Debugger extension listed in the results.

Next, set a breakpoint on line 2 of

hello.py

by placing the cursor on the

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.

The debugger will start, and then 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 can see that the

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

Working with Python interpreters

Select and activate an environment

The Python extension tries to find and then select what it deems the best environment for the workspace. If you would prefer to select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Note: If the Python extension doesn’t find an interpreter, it issues a warning. On macOS 12.2 and older, the extension also issues a warning if you’re using the OS-installed Python interpreter as it is known to have compatibility issues. In either case, you can disable these warnings by setting


python.disableInstallationCheck

to

true

in your user settings.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (

env

) that’s located within the workspace folder:

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see “(cached)” before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

If you have a folder or a workspace open in VS Code and you select an interpreter from the list, the Python extension will store that information internally. This ensures that the same interpreter will be used when you reopen the workspace.

The selected environment is used by the Python extension for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a

.py

file open in the editor, and opening a terminal with the Terminal: Create New Terminal command. In the latter case, VS Code automatically activates the selected environment.

Tip: To prevent automatic activation of a selected environment, add


"python.terminal.activateEnvironment": false

to your

settings.json

file (it can be placed anywhere as a sibling to the existing settings).

Tip: 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):


Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process

Note: By default, VS Code uses the interpreter selected for your workspace when debugging code. You can override this behavior by specifying a different path in the


python

property of a debug configuration. See Choose a debugging environment.

The selected interpreter version will show on the right side of the Status Bar.

The Status Bar also reflects when no interpreter is selected.

In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue so we can help determine the cause.

Manually specify an interpreter

If VS Code doesn’t automatically locate an interpreter you want to use, you can browse for the interpreter on your file system or provide the path to it manually.

You can do so by running the Python: Select Interpreter command and select the Enter interpreter path… option that shows on the top of the interpreters list:

You can then either enter the full path of the Python interpreter directly in the text box (for example, “.venv/Scripts/python.exe”), or you can select the Find… button and browse your file system to find the python executable you wish to select.

If you want to manually specify a default interpreter that will be used when you first open your workspace, you can create or modify an entry for the

python.defaultInterpreterPath

setting.

Note: Changes to the


python.defaultInterpreterPath

setting are not picked up after an interpreter has already been selected for a workspace; any changes to the setting will be ignored once an initial interpreter is selected for the workspace.

Additionally, if you’d like to set up a default interpreter to all of your Python applications, you can add an entry for

python.defaultInterpreterPath

manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set

python.defaultInterpreterPath

, which is in the Python extension section of User Settings, with the appropriate interpreter.

How the extension chooses an environment automatically

If an interpreter hasn’t been specified, then the Python extension automatically selects the interpreter with the highest version in the following priority order:

  1. Virtual environments located directly under the workspace folder.
  2. Virtual environments related to the workspace but stored globally. For example, Pipenv or Poetry environments that are located outside of the workspace folder.
  3. Globally installed interpreters. For example, the ones found in

    /usr/local/bin

    ,

    C:\\python38

    , etc.

Note: The interpreter selected may differ from what


python

refers to in your terminal.

If Visual Studio Code doesn’t locate your interpreter automatically, you can manually specify an interpreter.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations, in no particular order:

  • Standard install paths such as

    /usr/local/bin

    ,

    /usr/sbin

    ,

    /sbin

    ,

    c:\\python36

    , etc.
  • Virtual environments located directly under the workspace (project) folder.
  • Virtual environments located in the folder identified by the

    python.venvPath

    setting (see General Python settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of

    venvPath

    .
  • Virtual environments located in a

    ~/.virtualenvs

    folder for virtualenvwrapper.
  • Interpreters created by pyenv, Pipenv, and Poetry.
  • Virtual environments located in the path identified by

    WORKON_HOME

    (as used by virtualenvwrapper).
  • Conda environments found by

    conda env list

    . Conda environments which do not have an interpreter will have one installed for them upon selection.
  • Interpreters installed in a

    .direnv

    folder for direnv under the workspace folder.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Terminal command unless you change the

python.terminal.activateEnvironment

setting to

false

.

Please note that launching VS Code from a shell in which a specific Python environment is activated doesn’t automatically activate that environment in the default Integrated Terminal.

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal – Terminal profiles for how to change the shell.

Changing interpreters with the Python: Select Interpreter command doesn’t affect terminal panels that are already open. Thus, you can activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.

Choose a debugging environment

By default, the debugger will use the Python interpreter chosen with the Python extension. However, if there is a

python

property specified in the debug configuration of

launch.json

, it takes precedence. If this property is not defined, it will fall back to using the Python interpreter path selected for the workspace.

For more details on debug configuration, see Debugging configurations.

A quick introduction to the Visual Studio Code

Visual Studio Code is a lightweight source code editor. The Visual Studio Code is often called VS Code. The VS Code runs on your desktop. It’s available for Windows, macOS, and Linux.

VS Code comes with many features such as IntelliSense, code editing, and extensions that allow you to edit Python source code effectively. The best part is that the VS Code is open-source and free.

Besides the desktop version, VS Code also has a browser version that you can use directly in your web browser without installing it.

This tutorial teaches you how to set up Visual Studio Code for a Python environment so that you can edit, run, and debug Python code.

Install Python Extension

To make the VS Code work with Python, you need to install the Python extension from the Visual Studio Marketplace.

The following picture illustrates the steps:

  • First, click the Extensions tab.
  • Second, type the

    python

    keyword on the search input.
  • Third, click the

    Python

    extension. It’ll show detailed information on the right pane.
  • Finally, click the Install button to install the Python extension.

Now, you’re ready to develop the first program in Python.

Install and configure Visual Studio Code for Python development

Install and configure Visual Studio Code to create a development environment for learning to build Python applications.

Extensions view filters

The Extensions view search box supports filters to help you find and manage extensions. You may have seen filters such as

@installed

and

@recommended

if you used the commands Show Installed Extensions and Show Recommended Extensions. Also, there are filters available to let you sort by popularity or ratings and search by category (for example ‘Linters’) and tags (for example ‘node’). You can see a complete listing of all filters and sort commands by typing in the extensions search box and navigating through the suggestions:

Here are the Extensions view filters:


  • @builtin

    – Show extensions that come with VS Code. Grouped by type (Programming Languages, Themes, etc.).

  • @disabled

    – Show disabled installed extensions.

  • @installed

    – Show installed extensions.

  • @outdated

    – Show outdated installed extensions. A newer version is available on the Marketplace.

  • @enabled

    – Show enabled installed extensions. Extensions can be individually enabled/disabled.

  • @recommended

    – Show recommended extensions. Grouped as Workspace specific or general use.

  • @category

    – Show extensions belonging to specified category. Below are a few of supported categories. For a complete list, type

    @category

    and follow the options in the suggestion list:


    • @category:themes

    • @category:formatters

    • @category:linters

    • @category:snippets

These filters can be combined as well. For example: Use

@installed @category:themes

to view all installed themes.

If no filter is provided, the Extensions view displays the currently installed and recommended extensions.

Sorting

You can sort extensions with the

@sort

filter, which can take the following values:


  • installs

    – Sort by Marketplace installation count, in descending order.

  • name

    – Sort alphabetically by extension name.

  • publishedDate

    – Sort by extension published date.

  • rating

    – Sort by Marketplace rating (1-5 stars), in descending order.

  • updateDate

    – Sort by extension last update name.

Categories and tags

Extensions can set Categories and Tags describing their features.

You can filter on category and tag by using

category:

and

tag:

.

Supported categories are:

[Azure, Data Science, Debuggers, Education, Extension Packs, Formatters, Keymaps, Language Packs, Linters, Machine Learning, Notebooks, Others, Programming Languages, SCM Providers, Snippets, Testing, Themes, Visualization]

. They can be accessed through IntelliSense in the extensions search box:

Note that you must surround the category name in quotes if it is more than one word (for example,

category:"SCM Providers"

).

Tags may contain any string and are not provided by IntelliSense, so review the Marketplace to find helpful tags.

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.

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