Whatever you do in JetBrains DataSpell, you do that in the workspace. Your workspace can contain local notebooks and other files, attached directories, and attached projects.
Click the Attach new of existing directory link in the Workspace tool window.
Select File | Attach Directory from the main menu.
Click the icon on in the toolbar of the Workspace tool window.
Select the target directory in your systems. Click Ok to confirm your choice.
To attach a new directory to the workspace, select the File | New Workspace Directory from the main menu or right-click the workspace tree and select New | New Workspace Directory from the context menu. Then specify the directory name and its location.
Once you attach a directory, it appears in the Workspace tool window. You can open files that reside in it, or create new files (see how to add new Jupyter notebooks and Python files ).
On the Welcome Screen, click the Get from Version Control link.
From the main menu, select VCS | Get from Version Control.
Right-click the workspace tree and select Get from Version Control from the context menu
Select the version control system where your project is stored. Here it is Git:
Specify the path to the repository and select the directory to which a project will be cloned. Alternatively, you can select GitHub on the left, login using your credentials, and select any project you want to work with.
Click Clone.
Once you cloned a Git directory, JetBrains DataSpell create a Python virtual environment, so that you can work with your files.
If any of the attached directories requires a previously configured environment that is not currently available, JetBrains DataSpell shows a warning and provides two options: select an environment that fits the previous configuration or configure another Python interpreter (environment):
Note, when you have an environment based on the outdated version of Python, the following message appears:
Click Configure Python interpreter to set up a valid one.
Detach a directory
Right-click the target directory and select Detach Directory from the context menu.
Connect to a Jupyter server
Click the icon on the toolbar of the Workspace tool window to establish a connection to a Jupyter server.
In the New Jupyter Connection dialog, select the connection type:
Run local Jupyter server: run a Jupyter server in a local directory that will be attached to your workspace.
Connect to running Jupyter server: establish a connection to any locally run Jupyter server. The option is enabled if there is at least one active Jupyter server on your machine. Run jupyter notebook list in the Terminal window to check if there are any.
Connect by URL: establish a connection to a remote Jupyter server. The target URL should contain a server name or its address, and the access token.
Once the connection has been established, the server and its structure are shown in the Workspace tool window.
icon on in the toolbar of the Workspace tool window.
icon on the toolbar of the Workspace tool window to establish a connection to a Jupyter server.
