Get started

1. The following steps depend on your choice:

   Select any of the existing Conda interpreters. Alternatively, click and specify a path to the Conda executable in your file system, for example, C:\Users\jetbrains\Anaconda3\python.exe.

   • Specify the location of the new Conda environment in the text field, or click and find location in your file system. Note that the new Conda environment target directory must be empty!

   • Select the Python version from the list.

   • Specify the location of the Conda executable file in the text field, or click and find location in the Conda installation directory. You're basically looking for a path that you've used when installing Conda on your machine, for example, C:\Users\jetbrains\Anaconda3\python.exe.

   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.

   • 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 JetBrains DataSpell 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).

   

   In the Interpreter field, type the fully-qualified path to the required interpreter executable, or click and in the Select Python Interpreter dialog that opens, choose the desired Python executable and click OK.

   If JetBrains DataSpell 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).

   

   You will need administrator privileges to install, remove, and upgrade packages for the system interpreter. When attempting to install an interpreter package through an intention action, you might receive the following error message:

   

   As prompted, consider using a virtual environment for your project.

2. Once you configure an environment, click Launch DataSpell. JetBrains DataSpell creates a workspace so that you can start your work.

Create a notebook file

1. Do one of the following:

   • Right-click the target directory in the Workspace tool window, and select New from the context menu.

   • Press Alt+Insert

2. Select Jupyter Notebook.

3. In the dialog that opens, type a filename, example.

   

   A notebook document has the *.ipynb extension and is marked with the corresponding icon: .

   A newly created notebook opens in the editor. It contains one code cell. You can change its type with the cell type selector in the notebook toolbar:

   

   Each cell has a toolbar for quick access to the basic actions, such as code execution or navigation. By default, cell toolbars are disabled. To enable them, open project settings/preferences (Ctrl+Alt+S ), go to Jupyter, and select the Show cell toolbar checkbox.

Edit a notebook

1. To edit a cell, just click it.

2. Put some pandas code in the first code cell:

   import pandas as pd kernel_stats = pd.read_csv('libraries_by_python_version.csv') kernel_stats

3. You do not need to install the pandas package in advance. Just click a highlighted line, press Alt+Enter, and select a suggested fix for the missing import statement.

4. This example uses the libraries_by_python_version.csv dataset. Download it from libraries_by_python_version.csv and save in the project directory.

5. Add more code or Markdown cells to your notebook. You can add a code cell after the very last cell, add a code cell or Markdown cell right after the selected cell, and insert a new cell after executing the selected cell. You can find these actions in the Cell main menu item.

   

   Let's put some matplotlib code to visualize the data frame of the first code cell.

   import matplotlib.pyplot as plt plt.pie(kernel_stats['total_count'], labels=kernel_stats['library']) plt.show()

   

   Again, there is no need to preinstall matplotlib and numpy. Use Alt+Enter to fix imports.

You can edit code cells with the help of code insights, such as syntax highlighting, code completion, and so on.

Execute notebooks

You can execute the code of the notebook cells in many ways using the icons on the Jupyter notebook toolbar and cell toolbars, commands of the code cell context menu (right-click the code cell to open it), and the Run commands of the main menu.

1. Once you’ve executed the cell, its output is shown below the code.

   

   Note that when you work with local notebooks, you don’t need to launch any Jupyter server in advance: just execute any cell and the server will be launched.

   The Jupyter tool window shows the execution status in the Server tab and the current values of the variables in the Variables tab. Because the first cell contains a data frame, you can preview it in a tabular form - just click View as DataFrame link in the variable view.

2. Now execute the second cell. Its code depends on a variable from the first cell, so the order of cell execution is important.

   

   You can copy the built plot or save it as an image. To execute all cells, click on the notebook toolbar.

Debug

1. Click the gutter (the leftmost space in the editor) to set the breakpoints in the selected cell.

2. Press Shift+Alt+Enter for Windows/Linux or ⌥⇧↩ for macOS (or select the Debug Cell command from the extended set of actions in the cell toolbar). To debug the entire notebook, select Run | Debug from the main menu.

   

3. Use the stepping toolbar buttons to choose on which line you want to stop next and switch to the Debugger tool window to preview the variable values.

   

Configure servers

When you launch any Jupyter server, by default it uses the current project interpreter and the automatically selected port. However, you can select any other interpreter available in your JetBrains DataSpell instance and specify an alternative port. You can also connect to any configured server if you know its URL and token.

1. To open the server settings, select Configure Jupyter Server in the list of the Jupyter servers on the Jupyter notebook toolbar.

   

2. To connect to any running Jupyter server, select Configured Server and specify the server's path including a URL and a token.

3. In the Jupyter toolbar, from the list of the servers, select Switch to the current Jupyter Server to explicitly switch to the configured server.

See Manage Jupyter notebook servers for more details.

Change your environment

• Change the environment with the Python interpreter selector located in the lower-right corner of the JetBrains DataSpell UI. Click it and select the target environment from the list.

  

• Change Conda with Anaconda CLI

  1. In the Terminal window, run the ls command in the <Conda Home>/envs directory (for example, /Users/jetbrains/.conda/envs) and select the target environment.

  2. Navigate to the bin directory of your anaconda installation (for example, anaconda3/bin ).

  3. Execute the conda activate <env name> command (for example, conda activate my-conda-env ).

Create a new environment

1. Select Add interpreter in the Python interpreter selector.

   

2. In the Add Python Interpreter dialog, enter the name of the new environment, and specify the Anaconda base in the Conda executable field.

   

Install packages

1. In the Python interpreter selector, choose the target environment and select Interpreter Settings.

   

2. Click to add a new package. Click the Conda package manager button () to manage packages from the Conda repository. Otherwise, JetBrains DataSpell will be using pip.

3. Type a package name in the Search field and locate the target package. If needed, specify a package version.

4. Press Install. Close the window on the task completion.