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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.

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.

Insiders program

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 hello.py 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

Linting analyzes your Python code for potential errors, making it easy to navigate to and correct different problems.

The Python extension can apply a number of different linters including Pylint, pycodestyle, Flake8, mypy, pydocstyle, prospector, and pylama. See Linting.

Debugging

No more print statement debugging! 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 launch.json 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.

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. You can also use the python.pythonPath 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).

Installing packages

Packages are installed using the Terminal panel and commands like pip install <package_name> (Windows) and pip3 install <package_name> (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.

Jupyter notebooks

If you open a Jupyter notebook file (.ipynb) 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.

Testing

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.

Configuration

The Python extension provides a wide variety of settings for its various features. These are described on their relevant topics, such as Editing code, Linting, Debugging, and Testing. The complete list is found in the Settings reference.

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.

Run Python scripts,Jupyter notebooks, or even a graphical applicationin a full, remote Python environment.

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Find more details in thelist of installed Python libraries.

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As the name suggests, CoCalc's strength isonline code collaboration. Collaboration applies to editing plain Python files,Sage Worksheets,Jupyter Notebooks, and much more.

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CoCalc offers acomplete rewriteof the classicalJupyter notebook interface. It is tightly integrated into CoCalc and adds realtime collaboration,TimeTravel history and much more.

The user interface is very similar to Jupyter classic. It uses the same underlying Jupyter notebook file format, so you can download your *.ipynb file at any time and continue working locally.

There are severalPython environments available.

You can also easily runJupyter Classicaland JupyterLab in any CoCalc project.

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1. Upload or fetch your datasets,
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CoCalc has one-click code formatting for Jupyter notebooks and code files!

Your python code is formatted in a clean and consistent way usingyapf.

This reduces cognitive load reading source code, and ensures all code written by your team has a consistent and beautiful style.

Python code formatting works withpure .py filesand Jupyter Notebooks running a Python kernel.

Your existing Python scripts run on CoCalc. Either open aTerminal in the code editor, or click the 'Shell' button to open a Python command line.

Terminals also give you access togit andmany more utilities.

Regarding collaboration, terminals can be usedby multiple users at once. This means you can work with your coworkers in the same session at the same time. Everyone sees the same output, and coordinate viaside chat next to the terminal.

You can also simultaneously work with many terminal sessions.

For long-running programs, you can even close your browser and check on the result later.

Collaboration is a first class citizen on CoCalc. Useside chat for each file to discuss content with your colleagues or students.

Additionally, avatars give youpresence information about who is currently also working on a file.

Collaborators who are not online will be notified about new messages the next time they sign in.

Chat also supports markdown formatting and $\text{LaTeX}$LATE​X formulas.

CoCalc helps you share your work with the world. It offers its own hosting of shared documents, alongside with any associated data files.

You can configure if your published files should be listed publicly, or rather only be available via a confidential URL.

Snapshots are consistent read-only views of all your files in aCoCalc project. You can restore your files by copying back any that you accidentally deleted or corrupted.