Using Python environments in VS Code

An "environment" in Python is the context in which a Python program runs. An environment consists of an interpreter and any number of installed packages. The Python extension for VS Code provide helpful integration features for working with different environments.

Note: If you're looking to get started with Python in Visual Studio Code, refer to the Tutorial.

Select and activate an environment

By default, the Python extension looks for and uses 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.

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

Python: Select Interpreter command

You can switch environments at any time; switching environments helps you test different parts of your project with different interpreters or library versions as needed.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See Where the extension looks for environments in a later section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (env) that's located within the workspace folder:

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 adds an entry for python.pythonPath with the path to the interpreter inside your Workspace Settings. Because the path is part of the workspace settings, the same environment should already be selected whenever you open that workspace.

The Python extension uses the selected environment for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a .py file open in the editor, and opening a terminal with the Terminal: Create New Integrated Terminal command. In the latter case, VS Code automatically activated the selected environment.

Tip: To prevent automatic activation of a selected environment, add "python.terminal.activateEnvironment": false to your settings.json file.

Note: The python:pythonPath setting doesn't affect debugging. See Choose a debugging environment.

The Status Bar always 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, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue in the documentation repository so we can help determine the cause.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Integrated Terminal command unless you change the python.terminal.activateEnvironment setting to false.

However, launching VS Code from a shell in which a certain Python environment is activated does not automatically activate that environment in the default integrated terminal. Use the Terminal: Create New Integrated Terminal command after VS Code is running.

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal - Configuration for how to change the shell.

Any changes you make to an activated 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.

Changing interpreters with the Python: Select Interpreter command doesn't affect terminal panels that are already open. You can thus activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+\)) in the terminal title bar.

Choose a debugging environment

The python.pythonPath setting specifies the Python interpreter to use for debugging. Because the setting can exist in launch.json as well as workspace and user settings files, the following order of precedence is used:

  1. launch.json
  2. Workspace settings.json
  3. User settings.json

For more details, see Debugging.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations:

  • Standard install 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), which can contain multiple virtual environments. 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.

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

Global, virtual, and conda environments

By default, any Python interpreter that you've installed run in its own global environment, which is not specific to any one project. For example, if you just run python (Windows) or python3 (macOS/Linux) at a new command prompt, you're running in that interpreter's global environment. Accordingly, any packages that you install or uninstall affect the global environment and all programs that you run within that context.

Note: The Python Extension version 2018.8.1 and later automatically updates environments.

Although working in the global environment is an easy way to get started, that environment will, over time, become cluttered with many different packages that you've installed for different projects. Such clutter makes it difficult to thoroughly test an application against a specific set of packages with known versions, which is exactly the kind of environment you'd set up on a build server or web server.

For this reason, developers often create a virtual environment for a project. A virtual environment is a subfolder in a project that contains a copy of a specific interpreter. When you activate the virtual environment, any packages you install are installed only in that environment's subfolder. When you then run a Python program within that environment, you know that it's running against only those specific packages.

Tip: A conda environment is a virtual environments that's created and managed using the conda package manager. See Conda environments for more details.

To create a virtual environment, use the following command, where ".venv" is the name of the environment folder:

# macOS/Linux
# You may need to run sudo apt-get install python3-venv first
python3 -m venv .venv

# Windows
# You can also use py -3 -m venv .venv
python -m venv .venv

For examples of using virtual environment in projects, see the Django tutorial and the Flask tutorial.

Note: If you're using a version of the Python extension prior to 2018.10, and you create a virtual environment in a VS Code terminal, you must run the Reload Window command from the Command Palette and then use Python: Select Interpreter to activate the environment. If you have any problems with VS Code recognizing a virtual environment, please file an issue in the documentation repository so we can help determine the cause.

Tip: When you're ready to deploy the application to other computers, you can create a requirements.txt file with the command pip freeze > requirements.txt (pip3 on macOS/Linux). The requirements file describes the packages you've installed in your virtual environment. With only this file, you or other developers can restore those packages using pip install -r requirements.txt (or, again, pip3 on macOS/Linux). By using a requirements file, you need not commit the virtual environment itself to source control.

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, the Python extension automatically detects existing conda environments provided that the environment contains a Python interpreter. For example, the following command creates a conda environment with the Python 3.4 interpreter and several libraries, which VS Code then shows in the list of available interpreters:

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

Tip: If you create a new conda environment while VS Code is running, use the Reload Window command to refresh the environment list shown with Python: Select Interpreter; otherwise you may not see the environment there. It might take a short time to appear; if you don't see it at first, wait 15 seconds then try using the command again.

In contrast, if you fail to specify an interpreter as with conda create --name env-00, the environment won't appear in the list.

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.

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 Workspace Settings settings.json file:

  1. Select the File (Code on macOS) > Preferences > Settings menu command (⌘, (Windows, Linux Ctrl+,)) to open your Settings, select Workspace, and then do any of the following:

  2. Create or modify an entry for python.pythonPath with the full path to the Python executable:

    For example:

    • Windows:

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

      "python.pythonPath": "/home/python36/python"
      
  3. You can also use python.pythonPath to point to a virtual environment, for example:

    Windows:

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

    macOS/Linux:

    {
        "python.pythonPath": "/home/abc/dev/ala/venv/bin/python"
    }
    
    
  4. You can use an environment variable in the path setting using the syntax ${env:VARIABLE}. For example, if you've created a variable named PYTHON_INSTALL_LOC with a path to an interpreter, you can then use the following setting value:

    {
        "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.

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 for comments. Multiline values are not supported.

By default, the Python extension loads a file named .env in the current workspace folder, as identified by the default entry "python.envFile": "${workspaceFolder}/.env" in your user settings (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 - Set configuration 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.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. The value of PYTHONPATH can contain multiple path values separated by os.pathsep (semicolons on Windows, colons on Linux/macOS). Invalid paths are ignored.

In VS Code, PYTHONPATH affects debugging, linting, IntelliSense, unit testing, and any other operation that depends on Python resolving modules. For example, suppose you have source code in a src folder and tests in a tests folder. When running tests, however, they can't normally access modules in src unless you hard-code relative paths. To solve this, add the path to src to PYTHONPATH.

It's recommended that you set the PYTHONPATH variable in an Environment variable definitions file, described earlier.

Note: PYTHONPATH does not, repeat not, specify a path to a Python interpreter itself, and thus you never use it with the python.pythonPath setting. Clearly, the environment variable was badly named, but...c'est la vie. So make sure to read the PYTHONPATH documentation several times and fix in your mind that PYTHONPATH is not a path to an interpreter.

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.