Level up your Python game with JMP

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Do you use JMP and Python? Are you using them independently? Python for certain things…JMP for others? Did you know that there are many ways they work hand in hand?

This blog series focuses on highlighting a few ways that JMP and Python work together when tackling a problem. We want our users to be aware of these synergies so that they can effectively use both tools together to solve bigger problems faster.

It has been possible to use JMP and Python together for several versions. With JMP 18, the integration is more seamless because JMP comes with Python installed. You can now edit and submit a Python script directly in JMP. There are also new "JMP aware" Python packages that allow you to run JSL from Python, create JMP data tables, access data in a JMP data table directly, and more. Commonly used Python packages are now easier to install as well. (You can learn more about the enhanced Python integration in JMP 18 here, and if you want to learn the basics of how to use Python and JMP, check out my colleague's blog post Getting started with Python integration in JMP 18 .)

The following are some of the ways that JMP and Python work hand in hand that we describe in this blog series:

1. Visually cleaning data in JMP for analysis in Python.
2. Customizing decision tree models.
3. Visualizing and communicating Python models in JMP.
4. Screening models in JMP for final build out in Python.
5. Designing hyperparameter tuning tables in JMP for Python.