The extension looks for virtual environments in the first-level subfolders of venvPath. Virtual environments located in the folder identified by the python.venvPath setting (see General Python settings), which can contain multiple virtual environments.Virtual environments located directly under the workspace (project) folder. Standard install paths such as /usr/local/bin, /usr/sbin, /sbin, c:\\python36, etc.The extension automatically looks for interpreters in the following locations, in no particular order: Where the extension looks for environments If Visual Studio Code doesn't locate your interpreter automatically, you can manually specify an interpreter. Note: The interpreter selected may differ from what python refers to in your terminal. For example, the ones found in /usr/local/bin, C:\\python38, etc. For example, Pipenv or Poetry environments that are located outside of the workspace folder. Virtual environments related to the workspace but stored globally.Virtual environments located directly under the workspace folder.If an interpreter hasn't been specified, then the Python extension automatically selects the interpreter with the highest version in the following priority order: Generally used for data science projects. It can be used to manage both packages and virtual environments. It's installed with Python 3.9 by default (unless you are on a Debian-based OS install python3-pip in that case).Īllows 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 python3-venv in that case) The Python package manager that installs and updates packages. The following table lists the various tools involved with Python environments: Tool Whether to use a conda environment or a virtual one will depend on your packaging needs, what your team has standardized on, etc. Conda environmentsĪ conda environment is a Python environment that's managed using the conda package manager (see Getting started with conda). 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. When you install packages into a virtual environment it will end up in this new folder so that they are not interspersed with other packages used or needed by other workspaces. A virtual environment creates a folder that contains a copy (or symlink) to a specific interpreter. Virtual environmentsĪ virtual environment is a built-in way to create an environment to isolate the packages you install per workspace. This lets you isolate what packages you install for your workspace so that they don't interfere with your needs in another workspace. Both types of environment allow you to install packages without affecting other environments. There are two types of environments that you can create for your workspace: virtual and conda environments. You typically want to create an environment for each workspace. Any packages that you install or uninstall affect the global environment and all programs that you run within it.ĭo note that if you install packages into your global environment, though, in time it will become crowded with potentially unrelated or unexpected packages and make it difficult to properly test an application. 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. Python environments Global environmentsīy default, any Python interpreter installed runs in its own global environment. Note: If you'd like to become more familiar with the Python programming language, review More Python resources. An "environment" in Python is the context in which a Python program runs and consists of an interpreter and any number of installed packages. This article discusses the helpful Python environments features available in Visual Studio Code. Configure IntelliSense for cross-compilingĮdit Using Python environments in VS Code.
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