Table of contents

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 Watson Studio Local.

You can use these IBM visualization libraries and tools:

Create graphs with a one-word command and then explore them with an integrated UI instead of code. Run Scala code within Python notebooks.

PixieDust is an open source Python helper library that works as an add-on to Jupyter notebooks to improve the user experience of working with data.

For more details on all that PixieDust offers to users and developers, see PixieDust's README on GitHub .

Create interactive graphs with simple code.
SPSS model visualizations
Create interactive tables and charts to help you evaluate and improve a predictive analytics model created with SPSS machine learning algorithms.

You can also install other visualization libraries and packages. See Install custom or third-party libraries and packages.