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.

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 …

A quick start guide to get you up and coding with the Python extension in Visual Studio Code.

The VS Code Native REPL for Python builds upon the classic Python REPL and provides additional features, such as Intellisense and syntax highlighting to make your Python development experience …

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: …

When you've activated an environment with Jupyter installed, you can open a Jupyter notebook file (.ipynb) in VS Code and then convert it to Python code. Once you've converted the file, you can run …

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 numerous languages and a wide variety of …

To work with Python in Jupyter Notebooks, you must activate an Anaconda environment in VS Code, or another Python environment in which you've installed the Jupyter package.

The Python extension builds on the built-in testing features in VS Code and provides test discovery, test coverage, and running and debugging tests for Python's built-in unittest framework and pytest.

Linting will automatically run when a Python file is opened or saved. Errors and warnings are shown in the Problems panel (⇧⌘M (Windows, Linux Ctrl+Shift+M)) for open files, and are also highlighted in …