nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

758400_en-US

liliconcom

Pass a JMP datatable to Python as a Pandas dataframe

In prior versions of JMP (pre-JMP 18), when you passed a JMP datatable to Python, it was a Pandas data frame by default (very nice). Unfortunately it seems like that feature has been eliminated in JMP 18 where after passing the data table to Python with PythonSend(), you then need to loop through every column and covert to a Numpy array doing something like this:

df = pd.DataFrame()
for i in range( len(cols) ):
	df[ cols[i].name ] = np.array( cols[i] )

And this is time consuming for large data tables. I have received this negative feedback from a customer (NVIDIA) who is heavily utilizing JMP and Python.

Is there a way to pass data tables to Python as a Pandas data frame without having to do this conversion? Or better yet, can we bring back the JMP 17 functionality of PythonSend() which does this by default?