Configure a virtual environment
IntelliJ IDEA makes it possible to use the virtualenv tool to create a project-specific isolated virtual environment. The main purpose of virtual environments is to manage settings and dependencies of a particular project regardless of other Python projects. virtualenv tool comes bundled with IntelliJ IDEA, so the user doesn't need to install it.
Create a virtual environment
Navigate toor press Ctrl+Alt+Shift+S.
In the Project Structure dialog, select SDKs under the Platform Settings section, click , and choose Add Python SDK from the popup menu.
In the left-hand pane of the Add Python Interpreter dialog, select Virtualenv Environment. The following actions depend on whether the virtual environment existed before.
If New Virtualenv is selected:
Specify the location of the new virtual environment in the text field, or click and find location in your file system. Note that the directory where the new virtual environment should be located, must be empty!
Choose the base interpreter from the list, or click and find a Python executable in your file system.
If IntelliJ IDEA detects no Python on your machine, it provides two options: to download the latest Python versions from python.org or to specify a path to the Python executable (in case of non-standard installation).
Select the Inherit global site-packages checkbox if you want that all packages installed in the global Python on your machine to be added to the virtual environment you're going to create. This checkbox corresponds to the
--system-site-packagesoption of the virtualenv tool.
If Existing environment is selected:
Expand the Interpreter list and select any of the existing interpreters. Alternatively, click and specify a path to the Python executable in your file system, for example, C:\Python36\python.exe.
Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in IntelliJ IDEA.
Click OK to complete the task.
You can create as many virtual environments as required. To easily tell them from each other, use different names.
This approach is particularly helpful when you want to upgrade a version of Python your environment is based on, for example, from 3.5 to 3.9. You can specify a new base interpreter and use requirements.txt to ensure all the needed packages are installed.
For any of the configured Python interpreters (but Docker-based), you can: