Configure a virtual environment
PyCharm 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 PyCharm, so the user doesn't need to install it.
Create a virtual environment
Ensure that you have downloaded and installed Python on your computer.
Press Ctrl+Alt+S to open the project Settings/Preferences.
In the Settings/Preferences dialog Ctrl+Alt+S, select . Click the icon and select Add.
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 environment 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 the your file system.
If PyCharm 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 to inherit your global site-packages directory. This checkbox corresponds to the
--system-site-packagesoption of the virtualenv tool.
Select the Make available to all projects checkbox, if needed.
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 checkbox Make available to all projects, if needed.
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.
For any of the configured Python interpreters (but Docker-based), you can:
Once you have create a new virtual environment, you can reuse it for your other projects. Learn more how to setup an existing environment as a project interpreter.