Configuring Python environments

The Python extension relies on a Python environment (an interpreter and installed packages) for IntelliSense, auto-completions, linting, formatting, and any other language-related features other than debugging. The selected environment is also automatically activated when using the Python: Run Python File in Terminal and Python: Create Terminal commands.

Installing (or uninstalling) a package in the Terminal with a command like pip install matplotlib installs (or uninstalls) the package in the current environment.

Note: By default, the Python extension looks for and uses on the first Python interpreter it finds in the system path. If it doesn't find an interpreter, it issues a warning. On macOS, the extension also issues a warning if you're using the OS-installed Python interpreter, because you typically want to use an interpreter you install directly. In either case, you can disable these warnings by setting python.disableInstallationCheck to true in your user settings.

How to choose an environment

VS Code makes it easy to switch between multiple environments, allowing you to test different parts of your project with different interpreters as needed.

To use a specific interpreter, select the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Python: Select Interpreter command

This command automatically looks for and displays a list of available Python interpreters, conda environments, and virtual environments. (See Where the extension looks for environments in a later section.) The following image, for example, shows several Anaconda and CPython installations along with one conda environment:

List of interpreters

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see "(cached)" before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

Selecting an interpreter from the list configures your Workspace Settings accordingly,specifically adding an entry for python.pythonPath with the path to the interpreter. The Status Bar shows the current interpreter.

Status Bar showing a selected interpreter

The Status Bar also reflects when no interpreter is selected.

No interpreter selected

In either case, selecting this area of the Status Bar displays a list of available interpreters.

Activate an environment in the Terminal

The currently selected interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. You can also use Python: Create Terminal to open a terminal with the current environment activated.

When an environment is activated in the terminal, any changes you make to the environment within the terminal are persistent. For example, using conda install <package> from the terminal with a conda environment activated installs the package into that environment permanently. Similarly, using pip install in a terminal with a virtual environment activated adds the package to that environment.

To avoid activating virtual and conda environments when using these terminal commands, change the python.terminal.activateEnvironment setting to false.

Note: Launching VS Code from a shell in which a certain Python environment is activated does not automatically activate that environment in the default Terminal. Use the Python: Create Terminal command after VS Code is running.

Choose a debugging environment

Although selecting a different environment changes the python.pythonPath value in your Workspace settings.json file, it doesn't affect the User settings file, which is used by default for debugging. To use a different interpreter for debugging, set the value for pythonPath in the debugger settings launch.json file. See Debugging.

Conda environments

A conda environment is a Python environment that's managed using the conda package manager (see Getting started with conda (conda.io)). Conda works very well to create environments with interrelated dependencies as well as binary packages. Unlike virtual environments, which are scoped to a project, conda environments are available globally on any given computer. This availability makes it easy to configure several distinct conda environments and then choose the appropriate one for any given project.

As noted earlier, Visual Studio automatically detects existing conda environments provided that the environment contains a Python interpreter.

For example, the following command creates a conda environment without an interpreter, so VS Code doesn't display it in the list of available interpreters:

conda create --name env-00

In contrast, the following command creates a conda environment with a the Python 3.4 interpreter and several libraries. Because the environment contains an interpreter (which you can see in the Anaconda envs/env-01 folder created by this command), VS Code includes it in its list:

conda create -n env-01 python=3.4 scipy=0.15.0 astroid babel

Again, run the Reload Window command in VS Code after creating a new conda environment so that it appears in the list of interpreters.

For more information on the conda command line, see Conda environments (conda.io).

Note: Although the Python extension for VS Code doesn't currently have direct integration with conda environment.yml files, VS Code itself is a great YAML editor.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations:

  • Standard paths such as /usr/local/bin, /usr/sbin, /sbin, c:\\python27, c:\\python36, etc.
  • Virtual environments located directly under the workspace (project) folder.
  • Virtual environments located in the folder identified by the python.venvPath setting (see General settings). The extension looks for virtual environments in the first-level subfolders of venvPath.
  • Interpreters installed by pyenv.
  • A pipenv environment for the workplace folder. If one is found then no other interpreters are searched for or listed as pipenv expects to manage all aspects of the environment.
  • Conda environments that contain a Python interpreter. VS Code does not show conda environments that don't contain an interpreter.
  • Interpreters installed in a .direnv folder for direnv under the workspace (project) folder.

You can also manually specify an interpreter if Visual Studio Code does not locate it automatically.

Tip: If you create a new conda environment while VS Code is running, use the Reload Window command to refresh the environment list.

The extension also loads an environment variable definitions file identified by the python.envFile setting. The default value of this setting is ${workspaceFolder}/.env.

Manually specify an interpreter

If VS Code does not automatically locate an interpreter you want to use, you can set the path to it manually in your User Settings settings.json file:

  1. Select the File > Preferences > Settings command (⌘, (Windows, Linux Ctrl+,)) to open your User Settings.

  2. Create or modify an entry for python.pythonPath with the full path to the Python executable (or the folder containing the executable).

    For example:

    • Windows:

      "python.pythonPath": "c:/python36/python.exe"
      
    • macOS/Linux:

      "python.pythonPath": "/home/python36/python"
      

Environment variables in the interpreter path

A system environment variable can be used in the path setting using the syntax ${env:VARIABLE}. For example:

{
    "python.pythonPath": "${env:PYTHON_INSTALL_LOC}"
}

By using an environment variable, you can easily transfer a project between operating systems where the paths are different. Just be sure to set the environment variable on the operating system first.

Virtual environments

To use a Python interpreter that's installed in a virtual environment, use the python.venvPath to point to the folder containing the virtual environment.

Alternately, you can point python.pythonPath directly to the interpreter in the virtual environment:

  1. Edit the python.pythonPath setting to point to the virtual environment. For example:

    Windows:

    {
        "python.pythonPath": "c:/dev/ala/venv/Scripts/python.exe"
    }
    

    macOS/Linux:

    {
        "python.pythonPath": "/home/xxx/dev/ala/venv/bin/python"
    }
    
  2. Configure the same python.pythonPath variable in launch.json; see Choosing a debugging environment earlier.

  3. Ensure that the libraries and modules you plan on using for linting are installed within the virtual environment.

Environment variable definitions file

An environment variable definitions file is a simple text file containing key-value pairs in the form of environment_variable=value, with # used to mark comments. Multi-line values are not supported.

By default, the Python extension loads a file named .env in the current workspace folder, as identified by the default value of the python.envFile setting (see General settings). You can change the python.envFile setting at any time to use a different definitions file.

A debug configuration also contains an envFile property that also defaults to the .env file in the current workspace (see Debugging - standard configuration and options). This property allows you to easily set variables for debugging purposes that replace those used in the default .env file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

dev.env file

# dev.env - development configuration

# API endpoint
MYPROJECT_APIENDPOINT=https://my.domain.com/api/dev/

# Variables for the database
MYPROJECT_DBURL=https://my.domain.com/db/dev
MYPROJECT_DBUSER=devadmin
MYPROJECT_DBPASSWORD=!dfka**213=

prod.env file

# prod.env - production configuration

# API endpoint
MYPROJECT_APIENDPOINT=https://my.domain.com/api/

# Variables for the database
MYPROJECT_DBURL=https://my.domain.com/db/
MYPROJECT_DBUSER=coreuser
MYPROJECT_DBPASSWORD=kKKfa98*11@

You can then set the python.envFile setting to ${workspaceFolder}/prod.env, then set the envFile property in the debug configuration to ${workspaceFolder}/dev.env.

Next steps

  • Editing code - Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
  • Debugging - Learn to debug Python both locally and remotely.
  • Unit testing - Configure unit test environments and discover, run, and debug tests.
  • Settings reference - Explore the full range of Python-related settings in VS Code.