DataSpell 2021.3 Help

Manage your workspace

Whatever you do in DataSpell, you do that in the workspace. Your workspace can contain local notebooks and other files, attached directories, and attached projects.

When you run DataSpell for the very first time, you begin your work with attaching a directory to the workspace. You can also start with connecting to a Jupyter server.

The initial view of the DataSpell workspace

Attach a directory

  1. Do one of the following:

    • Click the Attach new of existing directory link in the Workspace tool window.

    • Select File | Attach Directory from the main menu.

    • Click the Attach a directory icon on in the toolbar of the Workspace tool window.

  2. Select the target directory in your systems. Click Ok to confirm your choice.

  3. 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).

DataSpell automatically configures a default virtual environment, so that you can execute notebooks and scripts. You can change it or create a new virtual evnironment.

If you want to work with any projects created in IntelliJ IDEA based IDE, you can open them from disk.

Open your project from disk

  1. From the main menu, select File |Open.

  2. In the Open File or Project dialog that opens, specify the location of the desired project directory.

  3. Click OK. The selected directory will be attached to the workspace.

Open a directory from Git

  1. Do one of the following:

    • 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 or a directory and select Get from Version Control from the context menu

  2. Select the version control system where your project is stored. Here it is Git:

    Open a project from VCS
  3. Specify the path to the repository and select the directory to which a project will be cloned. Do not select the default DataSpell installation directory as the target local directory. Alternatively, you can select GitHub on the left, login using your credentials, and select any project you want to work with.

  4. Click Clone.

Once you cloned a Git directory, DataSpell creates a Python virtual environment, so that you can work with your files, and attaches the directory to the workspace.

If any of the attached directories requires a previously configured environment that is not currently available, DataSpell shows a warning and provides two options: select an environment that fits the previous configuration or configure another Python interpreter (environment):

A warning message with the options to configure a project interpreter

Note, when you have an environment based on the outdated version of Python, the following message appears:

notification on the unsupported version of the Python interpreter

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

  1. Click the Add connection icon on the toolbar of the Workspace tool window to establish a connection to a Jupyter server.

  2. 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.

    Add a remote connection to a Jupyter server
    Once the connection has been established, the server and its structure are shown in the Workspace tool window.
    Remote Jupyter server in the Workspace tool window

Once you have connected to a remote server, you can open, edit, and run remote notebooks.

Last modified: 19 March 2022