A Jupyter notebook is a web-based environment for interactive computing. You can use notebooks to 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, namely the data, the code computations that process the data, the visualizations of the results, and text and rich media to enhance understanding.
- Required service
- Watson Studio
- Required permissions
- Editor or Admin role in a project
- Tools
- Notebook editor
- Programming languages
- Notebook editor: Python and R
Data format: All types
- Code support is available for loading and accessing data from project assets for:
- Data assets, such as CSV, JSON and .xlsx and .xls files
- Database connections and connected data assets
See Data load support for the supported file and database types.
- Data size
- 5 GB. If your files are larger, you must load the data in multiple parts.
Working with notebooks
The notebook editor is largely used for interactive, exploratory data analysis programming and data visualization. Only one person can edit a notebook at a time. All other users can access opened notebooks in view mode only, while they are locked.
You can use the preinstalled open source libraries that come with the notebook runtime environments, add your own libraries, and benefit from the IBM libraries provided at no extra cost.
When your notebooks are ready, you can create jobs to run the notebooks directly from the notebook editor. Your job configurations can use environment variables that are passed to the notebooks with different values when the notebooks run.
Learn more
- Quick start: Analyze data in a Jupyter notebook
- Create notebooks in the notebook editor
- Runtime environments for notebooks
- Libraries and scripts
- Code and run notebooks
- Schedule a notebook
- Share and publish notebooks
Parent topic: Analyzing data and building models