Install Python For Visual Studio | Download And Install The Python Workload

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.

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.

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).

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Quickstart: Create a Python project from existing code

Once you’ve installed Python support in Visual Studio, it’s easy to bring existing Python code into a Visual Studio project.

Important

The process described here does not move or copy the original source files. If you want to work with a copy, duplicate the folder first.

Follow these steps to create a project from existing files.

Important

The following process doesn’t move or copy any original source files. If you want to work with a copy of your files, first duplicate the folder and then create the project.

1. Launch Visual Studio and select File > New > Project.

2. In the Create a new project dialog, search for python, and select the From Existing Python code template. Enter a project name and location, choose the solution to contain the project, and select Create.

3. In the Create New Project from Existing Python Code wizard, set the folder path to your existing code, set a filter for file types, and specify any search paths that your project requires, then select Next. If you don’t know the search paths, leave the field blank.

4. On the next page, select the startup file for your project. Visual Studio selects the default global Python interpreter and version. You can change the environment by using the dropdown menu. When you’re ready, select Next.

   Note

   The dialog shows only files in the root folder. If the file you want is in a subfolder, leave the startup file blank. You can set the startup file later in Solution Explorer, as described in a later step.

5. Select the location where you want to save the project file (a .pyproj file on disk). If applicable, you can also include autodetection of virtual environments and customize the project for different web frameworks. If you’re unsure of these options, leave the fields set to the defaults.

6. Select Finish.

   Visual Studio creates the project and opens it in Solution Explorer. If you want to move the .pyproj file to a different location, select the file in Solution Explorer, and then select File > Save As on the toolbar. This action updates file references in the project, but it doesn’t move any code files.

7. To set a different startup file, locate the file in Solution Explorer, right-click the file, and select Set as Startup File.

If desired, run the program by pressing Ctrl+F5 or selecting Debug > Start without Debugging.

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.

Other popular Python extensions

The Microsoft Python extension provides all of the features described previously in this article. Additional Python language support can be added to VS Code by installing other popular Python extensions.

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

The extensions shown above are dynamically queried. Click on an extension tile above to read the description and reviews to decide which extension is best for you. See more in the Marketplace.

Next steps

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

Prerequisites

• Ability to install programs locally.
• Basic familiarity with programming concepts.

• Introduction min
• Install Python 3 min
• Knowledge check min
• Summary min

Step 5: Install packages in your Python environment

Previous step: Run code in the debugger

The Python developer community has produced thousands of useful packages that you can incorporate into your own projects. Visual Studio provides a UI to manage packages in your Python environments.

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.

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.

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.

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

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.

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

Detect your environment

Visual Studio shows all known environments in the Python Environments window. It automatically detects updates to existing interpreters.

If Visual Studio doesn’t detect an installed environment, see Manually identify an existing environment.

If you want to provide new forms of detection for Python environments, see PTVS Environment Detection (github.com).

Registry entries

Visual Studio (all versions) automatically detects each installed Python interpreter and its environment by checking the registry according to PEP 514 – Python registration in the Windows registry. Python installations are typically found under the HKEY_LOCAL_MACHINE\SOFTWARE\Python (32-bit) and HKEY_LOCAL_MACHINE\SOFTWARE\WOW6432Node\Python (64-bit) key within nodes for the distribution, such as PythonCore (CPython) and ContinuumAnalytics (Anaconda).

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.

View environments

1. Select the View > Other Windows > Python Environments menu command. The Python Environments window opens as a peer to Solution Explorer and shows the different environments available to you. The list shows both environments that you installed using the Visual Studio installer and environments you installed separately. That includes global, virtual, and conda environments. The environment in bold is the default environment that’s used for new projects. For more information about working with environments, see How to create and manage Python environments in Visual Studio environments.

   Note

   You can also use the Ctrl+K, Ctrl+` keyboard shortcut to open the Python Environments window from the Solution Explorer window. If the shortcut doesn’t work and you can’t find the Python Environments window in the menu, it’s possible that you haven’t installed the Python workload. See How to install Python support in Visual Studio on Windows for guidance about how to install Python.

   With a Python project open, you can open the Python Environments window from Solution Explorer. Right-click Python Environments and select View All Python Environments.

2. Now, create a new project with File > New > Project, selecting the Python Application template.

3. In the code file that appears, paste the following code, which creates a cosine wave like the previous tutorial steps, only this time plotted graphically. You can also use the project you previously created and replace the code.

   
from math import radians import numpy as np # installed with matplotlib import matplotlib.pyplot as plt def main(): x = np.arange(0, radians(1800), radians(12)) plt.plot(x, np.cos(x), 'b') plt.show() main()


4. In the editor window, hover over the

   
numpy

   and
   
matplotlib

   import statements. You’ll notice that they aren’t resolved. To resolve the import statements, install the packages to the default global environment.

5. When you look at the editor window, notice that when you hover over the

   
numpy

   and
   
matplotlib

   import statements that they aren’t resolved. The reason is the packages haven’t been installed to the default global environment.

   For example, select Open interactive window and an Interactive window for that specific environment appears in Visual Studio.

6. Use the drop-down list below the list of environments to switch to the Packages tab.The Packages tab lists all packages that are currently installed in the environment.

Prerequisites

• Visual Studio supports Python version 3.7. While it’s possible to use an earlier version of Visual Studio to edit code written in earlier versions of Python, those versions of Python aren’t officially supported. Visual Studio features such as IntelliSense and debugging might not work with earlier versions of Python.

• For Visual Studio 2015 and earlier, use Python 3.5 or earlier. You must manually install one of the Python interpreters.

Anaconda distributions

Although Visual Studio offers to install the Anaconda distribution, your use of the distribution and other packages from Anaconda Repository are bound by the Anaconda Terms of Service. These terms might require some organizations to pay Anaconda for a commercial license, or else configure the tools to access an alternate repository. For more information, see the Conda channels documentation.

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 (*).

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.

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.

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.

Install packages using the Python Environments window

1. From the Python Environments window, select the default environment for new Python projects and choose the Packages tab. You’ll then see a list of packages that are currently installed in the environment.

2. Install

   
matplotlib

   by entering its name into the search field and then selecting the Run command: pip install matplotlib option. Running the command will install
   
matplotlib

   , and any packages it depends on (in this case that includes
   
numpy

   ).

3. Choose the Packages tab.

4. Consent to elevation if prompted to do so.

5. After the package is installed, it appears in the Python Environments window. The X to the right of the package uninstalls it.

6. Consent to elevation if prompted to do so.

7. After the package installs, it appears in the Python Environments window. The X to the right of the package uninstalls it.

   Note

   A small progress bar might appear underneath the environment to indicate that Visual Studio is building its IntelliSense database for the newly-installed package. The IntelliSense tab also shows more detailed information. Be aware that until that database is complete, IntelliSense features like auto-completion and syntax checking won’t be active in the editor for that package.

   Visual Studio 2017 version 15.6 and later uses a different and faster method for working with IntelliSense, and displays a message to that effect on the IntelliSense tab.

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

Install Python support in Visual Studio

Python support is available only on Visual Studio for Windows. On Mac and Linux, Python support is available through Visual Studio Code.

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.

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.

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.

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.

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.

A Deep Dive into the Phi-2 Model

Python List Size: 8 Different Methods for Finding the Length of a List in Python

An End-to-End ML Model Monitoring Workflow with NannyML in Python

Bex Tuychiev

15 min

Install Python interpreters

There are several options for installing Python interpreters to work with Visual Studio. You can install an interpreter when you install the Python workload, or you can install an interpreter after a workload is present. Interpreters can also be installed manually outside of the Visual Studio Installer.

When you install the Python development workload in Visual Studio 2017 and later, Python 3 (64-bit) also installs by default. As an option, you can choose to install the 32-bit or 64-bit version of Python 2 or Python 3, along with Miniconda (Visual Studio 2019) or Anaconda 2/Anaconda 3 (Visual Studio 2017). The steps for this type of installation are described in Install Python support in Visual Studio.

An alternate approach is to install standard Python interpreters by using the Add Environment feature in Visual Studio. This option is available in the Python Environments window and the Python toolbar.

Python interpreters can also be installed manually outside of the Visual Studio Installer. Suppose you install Anaconda 3 before you install Visual Studio. You don’t need to reinstall Anaconda through the Visual Studio Installer. You can also install a newer version of an interpreter if it isn’t yet listed in the Visual Studio Installer.

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.

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.

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.

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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.

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.

Test your install

Quickly check your installation of Python support:

1. Launch Visual Studio.

2. Select Alt + I to open the Python Interactive window.

3. In the window, enter the statement

   
2+2

   .

   The statement output

   displays in the window. If you don’t see the correct output, recheck your steps.

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!

Review Python interpreters

The following table lists Python interpreters that can be used with Visual Studio.

Interpreter	Description	Notes
CPython	The “native” and most commonly used interpreter, available in 32-bit and 64-bit versions (32-bit recommended). Includes the latest language features, maximum Python package compatibility, full debugging support, and interop with IPython. Review the considerations in Should I use Python 2 or Python 3? to help determine which version of Python to install.	Visual Studio 2015 and earlier don’t support Python 3.6 or later, and can return errors like Unsupported python version 3.6. For Visual Studio 2015 and earlier, use Python 3.5 or earlier.
IronPython	A .NET implementation of Python, available in 32-bit and 64-bit versions. Provides C#/F#/Visual Basic interop, access to .NET APIs, standard Python debugging (but not C++ mixed-mode debugging), and mixed IronPython/C# debugging.	IronPython doesn’t support virtual environments.
Anaconda	An open data science platform powered by Python. Includes the latest version of CPython and most of the difficult-to-install packages.	If you’re unable to decide on an interpreter, we recommend using Anaconda.
PyPy	A high-performance tracing JIT implementation of Python. Good for long-running programs and situations where you identify performance issues but can’t find other resolutions.	Works with Visual Studio but with limited support for advanced debugging features.
Jython	An implementation of Python on the Java Virtual Machine (JVM). Similar to IronPython, code running in Jython can interact with Java classes and libraries. However, many of the libraries intended for CPython might not be accessible.	Works with Visual Studio but with limited support for advanced debugging features.

Download and install the Python workload

Complete the following steps to download and install the Python workload.

1. Download and run the latest Visual Studio Installer for Windows. Python support is present in release 15.2 and later. If you have Visual Studio installed already, open Visual Studio and run the installer by selecting Tools > Get Tools and Features.

   Tip

   The Community edition is for individual developers, classroom learning, academic research, and open source development. For other uses, install Visual Studio Professional or Visual Studio Enterprise.

2. The installer provides a list of workloads that are groups of related options for specific development areas. For Python, select the Python development workload and select Install:

   Python installation options Description Python distributions Choose any combination of Python distribution that you plan to work with. Common options include 32-bit and 64-bit variants of Python 2, Python 3, Miniconda, Anaconda 2, and Anaconda 3. Each option includes the distribution’s interpreter, runtime, and libraries. Anaconda, specifically, is an open data science platform that includes a wide range of preinstalled packages. Visual Studio automatically detects existing Python installations. For more information, see The Python Environments window. Also, if a newer version of Python is available than the version shown in the installer, you can install the new version separately and Visual Studio detects it. Cookiecutter template support Install the Cookiecutter graphical UI to discover templates, input template options, and create projects and files. For more information, see Use the Cookiecutter extension. Python web support Install tools for web development including HTML, CSS, and JavaScript editing support, along with templates for projects using the Bottle, Flask, and Django frameworks. For more information, see Python web project templates. Python native development tools Install the C++ compiler and other necessary components to develop native extensions for Python. For more information, see Create a C++ extension for Python. Also install the Desktop development with C++ workload for full C++ support.

   By default, the Python workload installs for all users on a computer under:

   %ProgramFiles%\Microsoft Visual Studio\
   
   \
   
   Common7\IDE\Extensions\Microsoft\Python
   
   

   where
   
   is 2022 and
   
   is Community, Professional, or Enterprise.
   
   

   %ProgramFiles(x86)%\Microsoft Visual Studio\
   
   \
   
   Common7\IDE\Extensions\Microsoft\Python
   
   

   where
   
   is 2019 or 2017 and
   
   is Community, Professional, or Enterprise.
   
   

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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.

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.

Move an interpreter

If you move an existing interpreter to a new location by using the file system, Visual Studio doesn’t automatically detect the change.

• If you originally specified the location of the interpreter through the Python Environments window, you can edit its environment by using the Configure tab in that window to identify the new location. For more information, see Manually identify an existing environment.

• If you installed the interpreter by using an installer program, use the following steps to reinstall the interpreter in the new location:

  1. Restore the Python interpreter to its original location.
  2. Uninstall the interpreter by using its installer, which clears the registry entries.
  3. Reinstall the interpreter at the new location.
  4. Restart Visual Studio, which should autodetect the new location in place of the old location.

This process ensures that the registry entries that identify the interpreter’s location, which Visual Studio uses, are properly updated. Using an installer also handles any other side effects that might exist.

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.

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.

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.

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.

Test your install

Quickly check your installation of Python support:

1. Launch Visual Studio.

2. Select Alt + I to open the Python Interactive window.

3. In the window, enter the statement

   
2+2

   .

   The statement output

   displays in the window. If you don’t see the correct output, recheck your steps.

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.

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!

Feedback

Submit and view feedback for

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.

Download and install the Python workload

Complete the following steps to download and install the Python workload.

1. Download and run the latest Visual Studio Installer for Windows. Python support is present in release 15.2 and later. If you have Visual Studio installed already, open Visual Studio and run the installer by selecting Tools > Get Tools and Features.

   Tip

   The Community edition is for individual developers, classroom learning, academic research, and open source development. For other uses, install Visual Studio Professional or Visual Studio Enterprise.

2. The installer provides a list of workloads that are groups of related options for specific development areas. For Python, select the Python development workload and select Install:

   Python installation options Description Python distributions Choose any combination of Python distribution that you plan to work with. Common options include 32-bit and 64-bit variants of Python 2, Python 3, Miniconda, Anaconda 2, and Anaconda 3. Each option includes the distribution’s interpreter, runtime, and libraries. Anaconda, specifically, is an open data science platform that includes a wide range of preinstalled packages. Visual Studio automatically detects existing Python installations. For more information, see The Python Environments window. Also, if a newer version of Python is available than the version shown in the installer, you can install the new version separately and Visual Studio detects it. Cookiecutter template support Install the Cookiecutter graphical UI to discover templates, input template options, and create projects and files. For more information, see Use the Cookiecutter extension. Python web support Install tools for web development including HTML, CSS, and JavaScript editing support, along with templates for projects using the Bottle, Flask, and Django frameworks. For more information, see Python web project templates. Python native development tools Install the C++ compiler and other necessary components to develop native extensions for Python. For more information, see Create a C++ extension for Python. Also install the Desktop development with C++ workload for full C++ support.

   By default, the Python workload installs for all users on a computer under:

   %ProgramFiles%\Microsoft Visual Studio\
   
   \
   
   Common7\IDE\Extensions\Microsoft\Python
   
   

   where
   
   is 2022 and
   
   is Community, Professional, or Enterprise.
   
   

   %ProgramFiles(x86)%\Microsoft Visual Studio\
   
   \
   
   Common7\IDE\Extensions\Microsoft\Python
   
   

   where
   
   is 2019 or 2017 and
   
   is Community, Professional, or Enterprise.
   
   

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