1. Jon K. Peck Technical Advisor SPSS Inc. peck@spss.com May, 2006
Programmability in SPSS 14: A Radical Increase in Power A Platform for Statistical Applications
Jon K. Peck
Technical Advisor SPSS Inc.
May, 2006
The plan is as follows:
We will first look at the new set of four general power features, focusing mainly on programmability.
We will look at some code in the demos, but the focus today is on what you can do with a bit of the how to.
Now let’s look at the five power features.
Copyright (c) SPSS Inc, 2006

2. GPL provides new programming power for graphics.
The Five Big Things
External Programming Language (BEGIN PROGRAM)
Multiple Datasets
XML Workspace and OMS Enhancements
Dataset and Variable Attributes
Drive SPSS Processor Externally
Working together, they dramatically increase the power of SPSS.
SPSS becomes a platform that enables you to build statistical/data manipulation applications.
The external programming language is the focus of this talk, but it interacts closely with other features
Not to mention GPL for graphics.
First a brief discussion of #2 through #5, then we will go into #1.
GPL provides new programming power for graphics.
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3. Multiple Datasets Many datasets open at once
One is active at a time (set by syntax or UI)
DATASET ACTIVATE command
Each dataset has a Data Editor window
Copy, paste, and merge between windows
Write tabular results to a dataset using Output Management System
Retrieve via Programmability
No longer necessary to organize jobs linearly
No need to open a dataset and save and close it in order to open another.
match/add can work with the active data and other datasets. Especially useful when merging non-SPSS datasets.
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4. XML Workspace Store dictionary and selected results in workspace
Write results to workspace as XML with Output Management System (OMS)
Retrieve selected contents from workspace via external programming language
Persists for entire session
Communication point
OMS can also create a new dataset that can be retrieved in the program as case data.
Use Xpath to select parts of objects or retrieve entire XML tree and parse in the external programming language.
We expect to do more with the workspace in future
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5. OMS Output: XML or Dataset
Write tabular results to Datasets with OMS
Main dataset remains active
Prior to SPSS 14, write to SAV file, close active, and open to use results
Tables can be accessed via workspace or as datasets
XML workspace and XPath accessors are very general
Accessed via programmability functions
Dataset output more familiar to SPSS users
Accessed via programmability functions or traditional SPSS syntax
Use with DATASET ACTIVATE command
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6. Attributes Extended metadata for files and variables
VARIABLE ATTRIBUTE, DATAFILE ATTRIBUTE
Keep facts and notes about data permanently with the data. E.g., validation rules, source, usage, question text, formula
Two kinds: User defined and SPSS defined
Saved with the data in the SAV file
Can be used in program logic
SPSS has metadata already: var labels, value labels, missing values etc.
Now users can create their own properties/attributes
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7. Programmability
Integrates external programming language into SPSS syntax
BEGIN PROGRAM … END PROGRAM
set of functions to communicate with SPSS
SPSS has integrated the Python language
SDK enabling other languages available
New: VB.NET available soon
External processes can drive SPSS Processor
VB.NET works only in this mode
SPSS Developer Central has SDK, Python Integration Plug-In, and many extension modules
Available for all SPSS 14 platforms
This is something radical.
These are different from input programs or transformation programs
SPSS has integrated Python, and, using the SDK, you can integrate other languages such as .NET if you want to use that.
Now let’s talk about the Python language a little.
Copyright (c) SPSS Inc, 2006

8. The Python Language
Free, portable, elegant, object oriented, versatile, widely supported, easy to learn,…
Download from Python.org.
Version or later required
Python tutorial
Python user discussion list
The Cheeseshop: Third-party modules
Python is not on the SPSS 14 cd or on Developer Central
Get it from the Python web site
SPSS does not control the Python language or its future development.
Copyright (c) SPSS Inc, 2006

9. Legal Notice
SPSS is not the owner or licensor of the Python software. Any user of Python must agree to the terms of the Python license agreement located on the Python web site.  SPSS is not making any statement about the quality of the Python program. SPSS fully disclaims all liability associated with your use of the Python program.
By opening SPSS to third party languages, we can take advantage of progress on those and do more, faster. Not limited by our own resources
Now what can you do with Programmability?
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10. Programmability Enables…
Generalized jobs by controlling logic based on
Variable Dictionary
Procedure output (XML or datasets)
Case data (requires SPSS )
Environment
Enhanced data management
Manipulation of output
Computations not built in to SPSS
Use of intelligent Python IDE driving SPSS (14.0.1)
statement completion, syntax checking, and debugging
External Control of SPSS Processor
Copyright (c) SPSS Inc, 2006

11. Programmability Makes Obsolete…
SPSS Macro
except as a shorthand for lists or constants
Learning Python is much easier than learning Macro
SaxBasic
except for autoscripts
but autoscripts become less important
These have not gone away.
The SPSS transformation language continues to be important.
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12. Demonstration
Code and supporting modules can be downloaded from SPSS Developer Central
examples are on the CD
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13. Initialization for Examples
* SPSS Directions, May * In preparation for the examples, specify where SPSS standard data files reside. BEGIN PROGRAM. import spss, spssaux spssaux.GetSPSSInstallDir("SPSSDIR") END PROGRAM.
This program creates a File Handle pointing to the SPSS installation directory, where the sample files are installed
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14. Example 0: Hello, world * EXAMPLE 0: My first program. BEGIN PROGRAM.
import spss
print "Hello, world!"
END PROGRAM.
Inside BEGIN PROGRAM, you write Python code.
import spss connects program to SPSS.
Import needed once per session.
Output goes to Viewer log items.
Executed when END PROGRAM reached.
Run
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15. Example 1: Run SPSS Command
*Run an SPSS command from a program; create file handle.
BEGIN PROGRAM.
import spss, spssaux
spss.Submit("SHOW ALL.") spssaux.GetSPSSInstallDir("SPSSDIR")
END PROGRAM.
Submit, in module spss is called to run one or more SPSS commands within BEGIN PROGRAM.
One of many functions (API's) that interacts with SPSS.
GetSPSSInstallDir, in the spssaux module, creates a FILE HANDLE to that directory
Run
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16. Example 2: Some API's
* Print useful information in the Viewer and then get help on an API.
BEGIN PROGRAM.
spss.Submit("GET FILE='SPSSDIR/employee data.sav'.")
varcount = spss.GetVariableCount()
casecount = spss.GetCaseCount()
print "The number of variables is " + str(varcount) + " and the number of cases is " + str(casecount)
print help(spss.GetVariableCount)
END PROGRAM.
There are API's in the spss module to get variable dictionary information.
help function prints short API documentation in Viewer.
Run
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17. Example 3a: Data-Directed Analysis
* Summarize variables according to measurement level.
BEGIN PROGRAM.
import spss, spssaux
spssaux.OpenDataFile("SPSSDIR/employee data.sav")
# make variable dictionaries by measurement level
catVars = spssaux.VariableDict(variableLevel=['nominal', 'ordinal'])
scaleVars = spssaux.VariableDict(variableLevel=['scale'])
print "Categorical Variables\n"
for var in catVars:
print var, var.VariableName, "\t", "var.VariableLabel"
Continued
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18. Example 3a (continued)
# summarize variables based on measurement level
if catVars:
spss.Submit("FREQ " + " ".join(catVars.variables))
if scaleVars:
spss.Submit("DESC "+" ".join(scaleVars.variables))
# create a macro listing scale variables
spss.SetMacroValue("!scaleVars", " ".join(scaleVars.variables))
END PROGRAM.
DESC !scaleVars.
" ".join(['x', 'y', 'z']) produces 'x y z'
Run
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19. Example 5: Handling Errors
* Handle an error. Use another standard Python module.
BEGIN PROGRAM.
import sys
try:
spss.Submit("foo.")
except:
print "That command did not work! ", sys.exc_info()[0]
END PROGRAM.
Errors generate exceptions
Makes it easy to check whether a long syntax job worked
Hundreds of standard modules and many others available from SPSS and third parties
Run
Copyright (c) SPSS Inc, 2006

20. Example 8: Create Basis Variables
* Create set of dummy variables for a categorical variable and a macro name for them.
BEGIN PROGRAM.
import spss, spssaux, spssaux2 mydict = spssaux.VariableDict()
spssaux2.CreateBasisVariables(mydict.["educ"], "EducDummy", macroname = "!EducBasis")
spss.Submit("REGRESSION /STATISTICS=COEF /DEP=salary" + "/ENTER=jobtime prevexp !EducBasis.")
END PROGRAM.
Uses dictionary object indexed by variable name.
The first category is omitted.
Discovers educ values from the data and generates appropriate transformation commands.
Creates macro !EducBasis
Run
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21. Example 9: Merge Directory Contents
* Automatically add cases from all SAV files in a directory. BEGIN PROGRAM.
import glob
savlist = glob.glob("c:/temp/parts/*.sav")
if savlist:
cmd = ["ADD FILES "] + ["/FILE='" + fn + "'" for fn in savlist] + [".", "EXECUTE."]
spss.Submit(cmd)
print "Files merged:\n", "\n".join(savlist)
else:
print "No files found to merge"
END PROGRAM.
The glob module resolves file-system wildcards
If savlist tests whether there are any matching files.
Run
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22. Example 10: Use Parts of Output - XML
* Run regression; get selected statistics, but do not display the regular Regression output. Use OMS and Xpath wrapper functions.
BEGIN PROGRAM.
import spss, spssaux
spssaux.OpenDataFile("SPSSDIR/CARS.SAV")
try:
handle, failcode = spssaux.CreateXMLOutput(\ "REGRESSION /DEPENDENT accel /METHOD=ENTER weight horse year.", visible=False)
horseCoef = spssaux.GetValuesFromXMLWorkspace(\
handle, "Coefficients", rowCategory="Horsepower", colCategory="B",cellAttrib="number")
print "The effect of horsepower on acceleration is: ", horseCoef
Rsq = spssaux.GetValuesFromXMLWorkspace(\ handle, "Model Summary", colCategory="R Square", cellAttrib="text")
print "The R square is: ", Rsq
spss.DeleteXPathHandle(handle)
except:
print "*** Regression command failed. No results available."
raise
END PROGRAM.
Example assumes that SPSS Output Labels are set to "Labels", not "Names".
A later example will show this using datasets instead of XML
Run
Copyright (c) SPSS Inc, 2006

23. Example 11: Transformations in Python Syntax
BEGIN PROGRAM.
import spss, Transform
spssaux.OpenDataFile('SPSSDIR/employee data.sav')
newvar = Transform.Compute(varname="average_increase",
varlabel="Salary increase per month of experience if at least a year",\
varmeaslvl="Scale",\
varmissval=[999,998,997],\
varformat="F8.4")
newvar.expression = "(salary-salbegin)/jobtime"
newvar.condition = "jobtime > 12"
newvar.retransformable=True
newvar.generate() # Get exception if compute fails
Transform.timestamp("average_increase")
spss.Submit("DISPLAY DICT /VAR=average_increase.")
spss.Submit("DESC average_increase.")
END PROGRAM.
Run
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24. Example 11A: Repeat Transform
BEGIN PROGRAM.
import spss, Transform
try:
Transform.retransform("average_increase")
Transform.timestamp("average_increase")
except:
print "Could not update average_increase."
else:
spss.Submit("display dictionary"+\ "/variable=average_increase.")
END PROGRAM.
Transformation saved using Attributes
Run
Copyright (c) SPSS Inc, 2006

25. Example 12: Controlling the Viewer Using Automation
BEGIN PROGRAM.
import spss, viewer
spss.Submit("DESCRIPTIVES ALL")
spssapp = viewer.spssapp()
try:
actualName = spssapp.SaveDesignatedOutput(\ "c:/temp/myoutput.spo")
except:
print "Save failed. Name:", actualName
else:
spssapp.ExportDesignatedOutput(\
"c:/temp/myoutput.doc", format="Word")
spssapp.CloseDesignatedOutput()
END PROGRAM.
Uses OLE automation methods. Client only, local mode
Run
Copyright (c) SPSS Inc, 2006

26. Example 13: A New Procedure Poisson Regression
BEGIN PROGRAM.
import spss, spssaux
from poisson_regression import *
spssaux.OpenDataFile(\ 'SPSSDIR/Tutorial/Sample_Files/autoaccidents.sav')
poisson_regression("accident", covariates=["age"], factors=["gender"])
END PROGRAM.
Poisson regression module built from SPSS CNLR and transformations commands.
PROGRAMS can get case data and use other Python modules or code on it.
Run
Copyright (c) SPSS Inc, 2006

27. Example 14: Using Case Data
* Mean salary by education level.
BEGIN PROGRAM.
import spssdata
data = spssdata.Spssdata(indexes=('salary', 'educ')) Counts ={}; Salaries={}
for case in data:
cat = int(case.educ)
Counts[cat] = Counts.get(cat, 0) + 1
Salaries[cat] = Salaries.get(cat,0) + case.salary
print "educ mean salary\n"
for cat in sorted(Counts):
print " %2d $%6.0f" % (cat, Salaries[cat]/Counts[cat])
del data
END PROGRAM.
Run
Copyright (c) SPSS Inc, 2006

28. Example 14a: Output As a Pivot Table
BEGIN PROGRAM.
# <accumulate Counts and Salaries as in Example 14>
desViewer = viewer.spssapp().GetDesignatedOutput()
rowcats = []; cells = []
for cat in sorted(Counts):
rowcats.append(int(cat))
cells.append(Salaries[cat]/Counts[cat])
ptable = viewer.PivotTable("a Python table",
tabletitle="Effect of Education on Salary",
caption="Data from employee data.sav",
rowdim="Years of Education",
rowlabels=rowcats,
collabels=["Mean Salary"],
cells = cells,
tablelook="c:/data/goodlook.tlo")
ptable.insert(desViewer)
END PROGRAM.
Run
Copyright (c) SPSS Inc, 2006

29. Exploring OMS Dataset Output
get file='c:/spss14/cars.sav'.
DATASET NAME maindata.
DATASET DECLARE regcoef.
DATASET DECLARE regfit.
OMS /IF SUBTYPE=["coefficients"]
/DESTINATION FORMAT = sav OUTFILE=regcoef.
OMS /IF SUBTYPE=["Model Summary"]
/DESTINATION FORMAT = sav OUTFILE=regfit.
REGRESSION /DEPENDENT accel /METHOD=ENTER weight horse year.
OMSEND.
Use OMS directly to figure out what to retrieve programmatically
Copyright (c) SPSS Inc, 2006

30. Example 10a: Use Bits of Output - Datasets
BEGIN PROGRAM.
import spss, spssaux, spssdata
try:
coefhandle, rsqhandle, failcode = spssaux.CreateDatasetOutput(\
"REGRESSION /DEPENDENT accel /METHOD=ENTER weight horse year.",
subtype=["coefficients", "Model Summary"])
cursor = spssdata.Spssdata(indexes=["Var2", "B"], dataset=coefhandle)
for case in cursor:
if case.Var2.startswith("Horsepower"):
print "The effect of horsepower on acceleration is: ", case.B
cursor.close()
Example assumes that SPSS Output Labels are set to "Labels", not "Names".
Copyright (c) SPSS Inc, 2006

31. Example 10a: Use Bits of Output – Datasets (continued)
cursor =spssdata.Spssdata(indexes=["RSquare"], dataset=rsqhandle)
row = cursor.fetchone()
print "The R Squared is: ", row.RSquare
cursor.close()
except:
print "*** Regression command failed. No results available."
raise
spssdata.Dataset("maindata").activate()
spssdata.Dataset(coefhandle).close()
spssdata.Dataset(rsqhandle).close()
END PROGRAM.
Run
Copyright (c) SPSS Inc, 2006

32. What We Saw Variable Dictionary access
Procedures selected based on variable properties
Actions based on environment
Automatic construction of transformations
Error handling
Variables that remember their formulas
Management of the SPSS Viewer
New statistical procedure
Access to case data
We used programmability to easily solve problems that were difficult to handle in earlier versions.
I asked at the beginning about challenges that have been hard to solve with SPSS. I hope that you have seen the glimmer of some solutions with SPSS 14.
Now that you are excited, how do you get started?
Copyright (c) SPSS Inc, 2006

33. Externally Controlling SPSS
SPSS Processor (backend) can be embedded and controlled by Python or other processes
Build applications using SPSS functionality invisibly
Application supplies user interface
No SPSS Viewer
Allows use of Python IDE to build programs
Pythonwin or many others
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34. PythonWin IDE Controlling SPSS
The PythonWin I D E is available from There are many other choices for a Python I D E.
Copyright (c) SPSS Inc, 2006

35. What Are the Programmability Benefits?
Extend SPSS functionality
Write more general and flexible jobs
Handle errors
React to results and metadata
Implement new features
Write simpler, clearer, more efficient code
Greater productivity
Automate repetitive tasks
Build SPSS functionality into other applications
Copyright (c) SPSS Inc, 2006

36. Python and Plug-In On the CD in SPSS 15
Getting Started
SPSS 14 ( for data access and IDE)
Python (visit Python.org)
Installation
Tutorial
Many other resources
SPSS® Programming and Data Management, 3rd Edition: A Guide for SPSS® and SAS® Users new
SPSS Developer Central
Python Plug-In ( version covers )
Example modules
Dive Into Python (diveintopython.org) book or PDF
Practical Python by Magnus Lie Hetland
Python Cookbook, 2nd ed by Martelli, Ravenscroft, & Ascher
Python and Plug-In On the CD in SPSS 15
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37. Recap Five power features of SPSS 14
Examples of programmability using Python
How to get started: materials and resources
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38. Questions ? ? ? ?
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39. In Closing
Working together these new features give you a dramatically more powerful SPSS.
SPSS becomes a platform that enables you to build your own statistical applications.
Programmability
Multiple datasets
XML Workspace and OMS enhancements
Attributes
External driver application
Copyright (c) SPSS Inc, 2006

40. Contact Jon Peck can now be reached at: peck@us.ibm.com
Copyright (c) SPSS Inc, 2006