With these Jupyter notebooks, you can explore the capability and value of using data science to discover valuable insights on a z/TPF system. They provide an interactive educational platform by containing a mixture of embedded documentation and executable Python code that can be run on a Jupyter Notebook environment. Common open-source libraries are used to show that collected z/TPF system metrics can be treated the same as other data sources in your enterprise environment.

For a quick and hassle-free demo of the notebooks, click the "launch binder" button (above) to create a Docker image with all of the necessary software components and start up a temporary Jupyter Notebook session with the files in this GitHub repository.

A Jupyter Notebook environment that contains the following libraries:

• Python (v3.5 or later)
• NumPy (v1.9 or later)
• Matplotlib (v3.0 or later)
• pandas (v0.24 or later)
• Seaborn (v0.9 or later)

The Anaconda Python distribution (v2018.12 or later) is recommended for a bundled installation of Jupyter Notebook and the required Python libraries.

This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 and the Apache License, Version 2.

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