Notebooks (Watson Studio)

A Jupyter notebook is a web-based environment for interactive computing. You can run small pieces of code that process your data, and you can immediately view the results of your computation. Notebooks include all of the building blocks you need to work with data:

The data
The code computations that process the data
Visualizations of the results
Text and rich media to enhance understanding

In Watson Studio, you can work with Jupyter notebooks in different tools:

Notebook editor: The notebook editor is largely used for interactive, exploratory data analysis programming and data visualization. You should use the notebook editor if you are new to Jupyter notebooks.

When you open a notebook in the notebook editor in edit mode, the notebook is locked while you are editing it so nobody else can edit the notebook at the same time.

While you hold the lock, only you can make changes to the notebook. All other projects users will see the lock icon on the notebook. Only project administrators are able to unlock a locked notebook and open it in edit mode.

When you close the notebook, the lock is released and another user can select to open the notebook in edit mode. Note that you must close the notebook while the runtime environment is still active. The notebook lock can’t be released for you if the runtime was stopped or is in idle state. If the notebook lock is not released for you, you can unlock the notebook from the project’s Assets page.
JupyterLab: JupyterLab offers an IDE-like development interface which includes notebooks. The modular structure of the interface is extensible and open to developers, allowing working with several open notebooks or files in tabs in the same window. JupyterLab is a high-performance, interactive development environment for creating and running Python notebooks. The integration with GIT supports collaboration and file sharing.

Working in the notebook editor

In the IBM Watson Studio notebook editor, you can create Python, Scala, and R notebooks to analyze your data.

Required service: None
Data format
Code support for loading and accessing data from:: CSV and JSON; Tables in all variants of IBM Db2, PostgreSQ, Microsoft SQL Server and many other popular database systems
Data size: 5 GB. If your files are larger, you must load the data in multiple parts.

Working in JupyterLab

In JupyterLab, you can create Python notebooks to analyze your data. To work in JupyterLab, you must associate the project with a Git repository and select to edit notebooks with the JuypterLab IDE.

Required service: None
Data format
Code support for loading and accessing data in data assets that have been added to the project from:: CSV and JSON; Tables in all variants of IBM Db2, PostgreSQ, Microsoft SQL Server and many other popular database systems

Notebook UI

A notebook

Code computations can build upon each other to quickly unlock key insights from your data. Notebooks record how you worked with data, so you can understand exactly what was done, reproduce computations reliably, and share your findings with others.

If you want to work on more than one notebook at the same time, you can open multiple notebooks on separate browser tabs. To open multiple notebooks, right-click the edit button and select open in a new tab. You can also collaborate with others on your notebooks, add comments, and view a history of your notebooks.

Notebook CLI commands

CPDCTL is a command-line interface (CLI) you can use to manage the lifecycle of notebooks. By using the notebook CLI, you can automate the flow for creating notebooks and running notebook jobs, moving notebooks between projects in Watson Studio, and adding custom libraries to notebook runtime environments.

Useful links:

Notebooks (Watson Studio)

Working in the notebook editor

Working in JupyterLab

Notebook UI

Notebook CLI commands

Learn more