Python Pandas and JMP

Question

Hi all

I discussed Python and JMP with JMP Partners. During the discussion, they asked two questions.

1. How to covert Python pandas columns to JMP Column (directly) without saving it ?

2. In case of doing Python Init(  ) in JMP, there is some problem that Pandas doesn't be imported

1) When we run Python Init() in JMP, we import like below.
       import pandas as _JMPpd
       (Python3.6 is coonected with JMP and Pandas is already installed in Python3.6)|
      Picture 1 is  the result of pandas import and version check
   2) But, there is some error in JMP, Picture 2

Other module(ex, sqlalchemy, pymssql etc) is OK, there is error only in Pandas.

Picture 2.jpg

Picture 1.jpg

IkjuShin · Answer

We discussed this problem again but it seems there is some error.

Python API doesn't seem to be stable. There is some error in pyplot library of matplotlib.

But OK in Python.

How about the following process ?

1. Starting JMP Process with JMP Python API

from win32com.client import Dispatch
 jmp = Dispatch("JMP.Application")
 jmp.visible=True #False

2. Working Python Pandas (starting data processing with Python)

3. The result of Pandas convert to JMP file

Fot this, JMP need to offer JMP Sned library in Python like Python Get()

4. Starting JMP Script

Starting Script after dt=Current Data Table()

Python.png

IkjuShin · Answer

Thanks Paul,

With your comment, I would have deep discussion time with our partner

Thanks again

Paul_Nelson · Answer

Well the previous anwser just showed getting a python object back, not getting the data from pandas as had intended. Here is a better example.  That borrows code from StackOverflow on getting data from dataframe to numpy.

https://stackoverflow.com/questions/13187778/convert-pandas-dataframe-to-numpy-array-preserving-index?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa

JMP has a bug, that it doesn't appear to be able to import a numpy array unless it's all the same data type...  So this example puts the data into the data frame, then gets it back out into another dictionary, which does transfer back to JMP.

Python Init();
Python Submit("\[
import pandas as pd
import numpy as np

print('Pandas Version: ' + pd.__version__)

data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada','Nevada'],'year':[2000,2001,2002,2001,2002,2003], 'pop':[1.5,1.7,3.6,2.4,2.9,3.2]
}
df = pd.DataFrame(data)
print(df)

v = df.values
print(v)

cols = df.columns
types = [(cols[i], df[k].dtype.type) for (i, k) in enumerate(cols)]
dtype = np.dtype(types)
array = np.zeros(v.shape[0], dtype)
for (i, k) in enumerate(array.dtype.names):
    array[k] = v[:, i]

print(array.dtype.names);
print(array)
d={}
l=[]
for (i, k) in enumerate(array.dtype.names):
    d[k] = v[:,i].tolist()

print(d)
print('leaving python')

]");
Print("inJSL");
pyData = Python Get(d);
Print(data);
Python Term();

Giving the following output

Pandas Version: 0.22.0
   pop   state  year
0  1.5    Ohio  2000
1  1.7    Ohio  2001
2  3.6    Ohio  2002
3  2.4  Nevada  2001
4  2.9  Nevada  2002
5  3.2  Nevada  2003
[[1.5 'Ohio' 2000]
 [1.7 'Ohio' 2001]
 [3.6 'Ohio' 2002]
 [2.4 'Nevada' 2001]
 [2.9 'Nevada' 2002]
 [3.2 'Nevada' 2003]]
('pop', 'state', 'year')
[(1.5, 'Ohio', 2000) (1.7, 'Ohio', 2001) (3.6, 'Ohio', 2002)
 (2.4, 'Nevada', 2001) (2.9, 'Nevada', 2002) (3.2, 'Nevada', 2003)]
{'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2], 'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002, 2003]}
leaving python

"inJSL"
["pop" => {1.5, 1.7, 3.6, 2.4, 2.9, 3.2}, "state" => {"Ohio", "Ohio", "Ohio", "Nevada", "Nevada", "Nevada"}, "year" => {2000, 2001, 2002, 2001, 2002, 2003}]
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