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.
Python Hello World Tutorial
Install Python and the Python extension
The tutorial guides you through installing Python and using the extension. You must install a Python interpreter yourself separately from the extension. For a quick install, use Python from python.org and install the extension from the VS Code Marketplace.
Once you have a version of Python installed, activate 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 Remote - 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.
The Insiders program allows you to try out and automatically install new versions of the Python extension prior to release, including new features and fixes.
If you'd like to opt into the program, you can either open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and select Python: Switch to Insiders Daily/Weekly Channel or else you can open settings (⌘, (Windows, Linux Ctrl+,)) and look for Python: Insiders Channel to set the channel to "daily" or "weekly".
Run Python code
To experience Python, create a file (using the File Explorer) named and paste in the following code:
The Python extension then provides shortcuts to run Python code in the currently selected interpreter (Python: Select Interpreter in the Command Palette):
- In the text editor: right-click anywhere in the editor and select Run Python File in Terminal. If invoked on a selection, only that selection is run.
- In Explorer: right-click a Python file and select Run Python File in Terminal.
You can also use the Terminal: Create New Terminal command to create a terminal in which VS Code automatically activates the currently selected interpreter. See Environments below. The Python: Start REPL activates a terminal with the currently selected interpreter and then runs the Python REPL.
For a more specific walkthrough on running code, see the tutorial.
Autocomplete and IntelliSense
The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion (in-context method and variable suggestions) across all your files and for built-in and third-party modules.
IntelliSense quickly shows methods, class members, and documentation as you type, and you can trigger completions at any time with ⌃Space (Windows, Linux Ctrl+Space). You can also hover over identifiers for more information about them.
Tip: Check out the IntelliCode extension for VS Code (preview). IntelliCode provides a set of AI-assisted capabilities for IntelliSense in Python, such as inferring the most relevant auto-completions based on the current code context.
Linting analyzes your Python code for potential errors, making it easy to navigate to and correct different problems.
The Python extension can apply a number of different linters including Pylint, pycodestyle, Flake8, mypy, pydocstyle, prospector, and pylama. See Linting.
No more statement debugging! Set breakpoints, inspect data, and use the debug console as you run your program step by step. Debug a number of different types of Python applications, including multi-threaded, web, and remote applications.
For Python-specific details, including setting up your configuration and remote debugging, see Debugging. General VS Code debugging information is found in the debugging document. The Django and Flask tutorials also demonstrate debugging in the context of those web apps, including debugging Django page templates.
The Python extension automatically detects Python interpreters that are installed in standard locations. It also detects conda environments as well as virtual environments in the workspace folder. See Configuring Python environments. You can also use the setting to point to an interpreter anywhere on your computer.
The current environment is shown on the left side of the VS Code Status Bar:
The Status Bar also indicates if no interpreter is selected:
The selected environment is used for IntelliSense, auto-completions, linting, formatting, and any other language-related feature other than debugging. It is also activated when you use run Python in a terminal.
To change the current interpreter, which includes switching to conda or virtual environments, select the interpreter name on the Status Bar or use the Python: Select Interpreter command.
VS Code prompts you with a list of detected environments as well as any you've added manually to your user settings (see Configuring Python environments).
Packages are installed using the Terminal panel and commands like (Windows) and (macOS/Linux). VS Code installs that package into your project along with its dependencies. Examples are given in the Python tutorial as well as the Django and Flask tutorials.
If you open a Jupyter notebook file () in VS Code, you can use the Jupyter Notebook Editor to directly view, modify, and run code cells.
You can also convert and open the notebook as a Python code file. The notebook's cells are delimited in the Python file with comments, and the Python extension shows Run Cell or Run All Cells CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:
Opening a notebook as a Python file allows you to use all of VS Code's debugging capabilities. You can then save the notebook file and open it again as a notebook in the Notebook Editor, Jupyter, or even upload it to a service like Azure Notebooks.
Using either method, Notebook Editor or a Python file, you can also connect to a remote Jupyter server for running the code. For more information, see Jupyter support.
The Python extension supports testing with unittest and pytest.
To run tests, you enable one of the frameworks in settings. Each framework also has specific settings, such as arguments that identify paths and patterns for test discovery.
Once discovered, VS Code provides a variety of commands (on the Status Bar, the Command Palette, and elsewhere) to run and debug tests, including the ability to run individual test files and individual methods.
The Python extension provides a wide variety of settings for its various features. These are described on their relevant topics, such as Editing code, Linting, Debugging, and Testing. The complete list is found in the Settings reference.
Other popular Python extensions
The Microsoft Python extension provides all of the features described previously in this article. Additional Python language support can be added to VS Code by installing other popular Python extensions.
- Open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)).
- 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.
Getting Started with Python in VS Code
In this tutorial, you use Python 3 to create the simplest Python "Hello World" application in Visual Studio Code. By using the Python extension, you make VS Code into a great lightweight Python IDE (which you may find a productive alternative to PyCharm).
This tutorial introduces you to VS Code as a Python environment, primarily how to edit, run, and debug code through the following tasks:
- Write, run, and debug a Python "Hello World" Application
- Learn how to install packages by creating Python virtual environments
- Write a simple Python script to plot figures within VS Code
This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on python.org within the context of VS Code for an introduction to the language.
If you have any problems, feel free to file an issue for this tutorial in the VS Code documentation repository.
To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:
- VS Code
- VS Code Python extension
- Python 3
Install Visual Studio Code and the Python Extension
If you have not already done so, install VS Code.
Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and it's published by Microsoft.
Install a Python interpreter
Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.
Install Python from python.org. You can typically use the Download Python button that appears first on the page to download the latest version.
Note: If you don't have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of Python 3.7, Python 3.8, Python 3.9, and Python 3.10. Be aware that you might have compatibility issues with some packages using this method.
For additional information about using Python on Windows, see Using Python on Windows at Python.org
The system install of Python on macOS is not supported. Instead, an installation through Homebrew is recommended. To install Python using Homebrew on macOS use 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.
The built-in Python 3 installation on Linux works well, but to install other Python packages you must install with get-pip.py.
Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.
Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you'll also want to install the Remote - WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.
Verify the Python installation
To verify that you've installed Python successfully on your machine, run one of the following commands (depending on your operating system):
Linux/macOS: open a Terminal Window and type the following command:
Windows: open a command prompt and run the following command:
If the installation was successful, the output window should show the version of Python that you installed.
Note You can use the command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).
Start VS Code in a project (workspace) folder
Using a command prompt or terminal, create an empty folder called "hello", navigate into it, and open VS Code () in that folder () by entering the following commands:
Note: If you're using an Anaconda distribution, be sure to use an Anaconda command prompt.
By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in , which are separate from user settings that are stored globally.
Alternately, you can run VS Code through the operating system UI, then use File > Open Folder to open the project folder.
Select a Python interpreter
Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.
From within VS Code, select a Python 3 interpreter by opening the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):
The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don't see the desired interpreter, see Configuring Python environments.
Note: When using an Anaconda distribution, the correct interpreter should have the suffix , for example .
Selecting an interpreter sets which interpreter will be used by the Python extension for that workspace.
Note: If you select an interpreter without a workspace folder open, VS Code sets in User scope instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.
Create a Python Hello World source code file
From the File Explorer toolbar, select the New File button on the folder:
Name the file , and it automatically opens in the editor:
By using the 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 :
When you start typing , notice how IntelliSense presents auto-completion options.
IntelliSense and auto-completions work for standard Python modules as well as other packages you've installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the variable contains a string, IntelliSense provides string methods when you type :
Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the variable and the call, and save the file (⌘S (Windows, Linux Ctrl+S)).
For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.
Run Hello World
It's simple to run with Python. Just click the Run Python File in Terminal play button in the top-right side of the editor.
The button opens a terminal panel in which your Python interpreter is automatically activated, then runs (macOS/Linux) or (Windows):
There are three other ways you can run Python code within VS Code:
Right-click anywhere in the editor window and select Run Python File in Terminal (which saves the file automatically):
Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.
From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.
Configure and run the debugger
Let's now try debugging our simple Hello World program.
First, set a breakpoint on line 2 of by placing the cursor on the call and pressing F9. Alternately, just click in the editor's left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.
Next, to initialize the debugger, press F5. Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.
Note: VS Code uses JSON files for all of its various configurations; is the standard name for a file containing debugging configurations.
These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.
You can also start the debugger by clicking on the down-arrow next to the run button on the editor, and selecting Debug Python File in Terminal.
The debugger will stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you will see now defined 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 , hovering over the variable will display the string in a box above the variable.
You can also work with variables in the Debug Console (If you don't see it, select Debug Console in the lower right area of VS Code, or select it from the ... menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:
Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. "Hello World" appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.
If you restart the debugger, the debugger again stops on the first breakpoint.
To stop running a program before it's complete, use the red square stop button on the debug toolbar (⇧F5 (Windows, Linux Shift+F5)), or use the Run > Stop debugging menu command.
For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.
Tip: Use Logpoints instead of print statements: Developers often litter source code with statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.
Install and use packages
Let's now run an example that's a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the and packages to create a graphical plot as is commonly done with data science. (Note that cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)
Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called , and paste in the following source code:
Tip: If you enter the above code by hand, you may find that auto-completions change the names after the keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.
Next, try running the file in the debugger using the "Python: Current file" configuration as described in the last section.
Unless you're using an Anaconda distribution or have previously installed the package, you should see the message, "ModuleNotFoundError: No module named 'matplotlib'". Such a message indicates that the required package isn't available in your system.
To install the package (which also installs as a dependency), stop the debugger and use the Command Palette to run Terminal: Create New Terminal (⌃⇧` (Windows, Linux Ctrl+Shift+`)). This command opens a command prompt for your selected interpreter.
A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:
Note: For additional information about virtual environments, see Environments.
Create and activate the virtual environment
Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.
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):
Select your new environment by using the Python: Select Interpreter command from the Command Palette.
Install the packages
Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:
Once you are finished, type in the terminal window to deactivate the virtual environment.
For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.
You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.
To learn more about the Python language, follow any of the programming tutorials listed on python.org within the context of VS Code.
To learn to build web apps with the Django and Flask frameworks, see the following tutorials:
There is then much more to explore with Python in Visual Studio Code:
Visual Studio Code for Python Programmers
Part I Welcome to Visual Studio Code 1
Chapter 1 Getting Started 3
Installing Visual Studio Code 4
The Visual Studio Code User Interface 4
Activity Bar 5
Side Bar 6
Status Bar 12
Command Palette 12
Color Themes and Icons 18
Display Langage 18
Chapter 2 Hello World for Python 21
Installing a Python Interpreter 21
Installing the Python Extension for Visual Studio Code 22
Creating a Python File 23
Selecting an Interpreter 24
Setting a Default Interpreter 26
Settings Editor 26
settings.json File 26
Selecting a Linter 26
Editing a Python File 27
Running a Python File 29
Workflow Recap 30
Chapter 3 Editing Code 33
Quick Fixes 34
Code Completion, Definitions, and Declarations 35
Edit Formatting Settings in the Settings Editor 39
Edit Formatting Settings in settings.json 40
Enable and Disable Linting 41
Run Linting 42
Linting Settings 43
Extract Variable 44
Extract Method 45
Sort Imports 46
Part II Additional Visual Studio Code Features 51
Chapter 4 Managing Projects and Collaboration 53
Files and Folders 53
Open a Project 54
Navigate Files 56
Search across Files 57
Close a File or Folder 60
Virtual Environments 61
Conda Environments 61
Source Control 63
Initialize a Repository 65
Commit Changes 66
Gutter Indicators 71
View Diffs 71
Push and Merge Commits 73
Pull Requests 74
Live Share 74
Install Live Share 75
Sign In to Live Share 76
Share a Project 76
Join a Session 78
Editing and Collaboration 80
Follow a Participant 80
Share a Terminal 81
Chapter 5 Debugging 83
Starting a Debug Session 84
Debug Commands 89
Step Over 90
Step Into 90
Step Out 91
Call Stack 92
Triggering a Breakpoint 93
The Debug Console 98
Launch Configurations 101
Chapter 6 Unit Testing 105
Enable and Discover Tests 105
Run Tests 109
Debug Tests 113
Chapter 7 Jupyter Notebook 117
Creating and Opening a Jupyter Notebook 118
Code Cell Modes 120
Adding Cells 121
Editing Cells 122
Running a Cell 124
Running a Single Cell 124
Running All Code Cells 124
Running Cells Above and Below a Code Cell 125
Additional Commands 126
Viewing Variables and Data 126
Viewing Plots 128
Debugging a Jupyter Notebook 129
Connecting to a Remote Server 130
Exporting a Notebook 131
Chapter 8 Using Git and GitHub with Visual Studio Code 135
Getting Started 135
GitHub Pull Requests and Issues Extension 136
Publish a Project to GitHub 139
Push Changes to GitHub 141
Manage Pull Requests and Issues 143
Pull Requests 144
Clone Repository 152
Timeline View 154
Chapter 9 Deploy a Django App to Azure App Service with the Azure App Service Extension 157
Getting Started 157
Creating a Django Project 159
Creating an App 161
Creating a Home Page 163
Creating Website Pages 166
Deploying to Azure 168
Chapter 10 Create and Debug a Flask App 177
Getting Started 177
Create a Flask App 178
Create and Render a Template 180
Debug the Flask App 184
Chapter 11 Create and Deploy a Container with Azure Container Registry and Azure App Service 189
Getting Started 189
Create a Container 191
Add Docker Files to the Project 191
Build an Image 193
Build and Run a Container 195
Debug a Container 197
Push an Image to the Registry 197
Create an Azure Container Registry 198
Determine the Image’s Registry Location 199
Deploy the Container Image to Azure 201
Make Changes to the App and Deploy 205
Multicontainer Apps 206
Chapter 12 Deploy an Azure Function Trigger by a Timer 209
Getting Started 210
Create an Azure Function 211
Invoke the Function Locally 213
Add the Code to the Function 214
Deploy the Function to Azure 215
Appendix Getting Started with Azure 221
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