In our quest to harness Python's extensive capabilities within JMP, we faced the challenge of integrating Python's flexibility with JMP’s intuitive and interactive user experience. Prior to JMP 18, we achieved this through a workaround ‒ running Python on a server to bypass JMP’s cumbersome Python integration. However, the release of JMP18 re-introduced Python integration, simplifying this process.

Building on our initial concept of enabling JMP users to leverage Python without requiring programming expertise, we developed JMPyFacade (JMPy). This tool offers a familiar interface, since it is similar to JMP's, for executing predefined Python services via dynamically generated user interface, all while abstracting the underlying Python and JSL code.

In this presentation, we explore the technical architecture of JMPyFacade and demonstrate how it effectively bridges the gap between JMP and Python. Attendees learn how Python can be leveraged within JMP for efficient and engaging data analysis.