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.
In this article. Previous step: Write and run code The Visual Studio Interactive window for Python provides a rich read-evaluate-print-loop (REPL) experience that greatly shortens the usual edit-build-debug cycle. The Interactive window provides all the capabilities of the REPL experience of the Python command line. It also makes it very easy to exchange code with source files in the Visual. Code with Mu: a simple Python editor for beginner programmers. Download Start Here. © 2021 Nicholas H.Tollervey.Mu wouldn't be possible without these people. The Python interactive console provides a space to experiment with Python code. You can use it as a tool for testing, working out logic, and more. For use with debugging Python programming files, you can use the Python code module to open up an interactive interpreter within a file, which you can read about in our guide How To Debug Python with.
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 3.7 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
hello.py and paste in the following code (assuming Python 3):
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 Integrated 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.
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.
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).
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.
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.
The Python extension supports testing with the unittest, pytest, and nose test frameworks.
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.
Repl Python Compiler
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.
Online Python Repl
Python Replace String
- 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.
Python Replace Nan With 0
MicroPython employs many advanced coding techniques, and lots of tricks to maintain a compact size while still having a full set of features.
Some of the more notable items are:
- highly configurable due to many compile-time configuration options
- support for many architectures (x86, x86-64, ARM, ARM Thumb, Xtensa)
- extensive test suite with over 590 tests, and more than 18,500 individual testcases
- code coverage at 98.4% for the core and at 96.3% for the core plus extended modules
- fast start-up time from boot to loading of first script (150 microseconds to get to boot.py, on PYBv1.1 running at 168MHz)
- a simple, fast and robust mark-sweep garbage collector for heap memory
- a MemoryError exception is raised if the heap is exhausted
- a RuntimeError exception is raised if the stack limit is reached
- support for running Python code on a hard interrupt with minimal latency
- errors have a backtrace and report the line number of the source code
- constant folding in the parser/compiler
- pointer tagging to fit small integers, strings and objects in a machine word
- transparent transition from small integers to big integers
- support for 64-bit NaN boxing object model
- support for 30-bit stuffed floats, which don't require heap memory
- a cross-compiler and frozen bytecode, to have pre-compiled scripts that don't take any RAM (except for any dynamic objects they create)
- multithreading via the '_thread' module, with an optional global-interpreter-lock (still work in progress, only available on selected ports)
- a native emitter that targets machine code directly rather than the bytecode virtual machine
- inline assembler (currently Thumb and Xtensa instruction sets only)