How to convert Python Pandas dataFrame to jmp format without saving it as files?

[#1]

I know JMP support "Python Interface" function within JMP script envirornment. But somtimes it will be better to do co-work independently Python engineer and JMP one.

In our case, Python engineer read database files with DB interface, and makes pre-process with Pandas library, and wants to convert JMP format because JMP enginner need only JMP format data in order to do data analysis and post-process with JSL.

As you imagine, the bottleneck is how to convert Python Pandas dataFrame without saving it as files. The easiest way is to save files and read it as jmp format but it takes many times to save and read them. I'd like to know where there is any library to convert Python Pandas dataFrame to jmp format directly in memory or not.

[#2]

JSON formats are too many to handle.

[#3]

Thanks for the post back.   Would love to be able to simply open a .parquet file directly with JMP. 

We already use DB connections to fetch data,  but there are so many inefficiencies in using .csv in a full data pipelines (extraction writing,  storing,  reading),  we'd love to start using parquet.