Visualizations in notebooks
Use visualizations in your notebooks to present data visually to help identify patterns, gain insights, and make decisions.
Many of your favorite open source visualization libraries, such as matplotlib, are pre-installed on Data Science Experience.
You can also install other visualization libraries and packages. See Install custom or third-party libraries and packages.
You can use these IBM visualization libraries and tools:
- PixieDust: Create graphs with a one-word command and then explore them with an integrated UI instead of code. Run Scala code within Python notebooks.
- Brunel: Create interactive graphs with simple code.
- SPSS models: Create interactive tables and charts to help you evaluate and improve a predictive analytics model created with SPSS machine learning algorithms.