Additional JSL-to-Python Examples

The following examples show how to send a data table to Python, create objects in Python, and use matrix operations in Python.

Send a Data Table to Python

This example sends a data table to Python and prints the file path and number of columns and rows to the log.

Names Default To Here(1);

dt = Open( "$SAMPLE_DATA/Big Class.jmp", invisible );

Python Send( dt ); // Send the opened data table represented by dt to Python

Python Submit( "print( dt )" );

print( dt )

Big Class (5 columns x 40 rows)

DataTable("Big Class")

Create Objects in Python

This example creates a Python object, retrieves the object into JMP, and uses the object to create a JMP data table.

Python Submit("\[

import numpy as np

import pandas as pd

# Generate basic series

ss = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

print(ss)

sn = pd.Series(np.random.randn(5))

print(sn)

# Generate Data Frames from Series

s1 = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

print(s1)

dfs1 = pd.DataFrame(s1)

print(dfs1)

d1 = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),

      'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}

print(d1)

dfd1 = pd.DataFrame(d1)

print(dfd1)

d2 = {'one' : [1., 2., 3., 4.],

      'two' : [4., 3., 2., 1.]}

print(d2)

dfd2 = pd.DataFrame(d2)

print(dfd2)

]\"

);

ss = Python Get( ss );

Show( ss );

sn = Python Get( sn );

Show( sn );

dfs1 = Python Get( dfs1 );

dfs1 << New Data View;

dfd1 = Python Get( dfd1 );

dfd1 << New Data View;

dfd2 = Python Get( dfd2 );

dfd2 << New Data View;

Matrix Operations in Python

Use of Python Get() and Python Send() with a Matrix requires the presence of the Python package NumPy. JMP automatically attempts to import this at runtime, requiring only the import of the package.

The following example sends a JSL matrix object to the Python environment.

Names Default To Here(1);

xx = [1 2 3, 3 4 5, 6 7 8];

Python Send(xx);

Python Submit("\[

import numpy

print(xx)

]\");

[[1. 2. 3.]

[3. 4. 5.]

[6. 7. 8.]]

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