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
Working with notebooks
In the IBM Watson Studio notebook editor, you can create Python, Scala, and R notebooks to analyze your data.
- Required service
- Watson Studio
- Data format
- Code support for loading and accessing data from:
- CSV and JSON
- Tables in IBM Db2 Warehouse on Cloud (previously named IBM dashDB) and Compose for PostgreSQL
- Data size
- 5 GB. If your files are larger, you must load the data in multiple parts.
For more information on choosing the right tool for your data and use case, see Choosing a tool.
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.