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Finally, we'll loop over both lists in parallel for generating our scatterplots. Variable names starting with “no_” are added to one list and those that start with “med_” go into the other. The syntax below sets up two empty Python lists and loops over all variable names in our data. GRAPH /SCATTERPLOT(BIVAR)=med_1 WITH no_1 /MISSING=LISTWISE. Like previously, we'll first generate syntax for just one scatterplot as shown below.Īfter removing all line breaks, these steps result in We'd now like to inspect scatterplots of reaction times of no alcohol versus medium alcohol over each of the 5 trials.
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for var in varList: spss.Submit(''' GRAPH /BAR(SIMPLE)=MEAN(%s) BY order. import spss,spssaux varSpec = "no_1 to hi_5" #Specify variables with SPSS TO or ALL keywords varDict = spssaux.VariableDict(caseless = True) varList = varDict.expand(varSpec) varList.sort(key = lambda x: varDict.VariableIndex(x)) print varList end program. *Retrieve variable names from data and print for inspection. However, a cleaner way to go is with the Python syntax below. For running similar charts over different reaction times, we could copy-paste the line and replace no_1 by no_2 and so on. Running this line results the first desired bar chart. We'll remove all line breaks from the pasted syntax, resulting in The interface and resulting syntax are wonderfully simple and often result in the exact same charts as the much more complex Chart Builder. We'll start by generating the syntax for the first chart from the menu as shown below.Īs a rule of thumb, try to use Legacy Dialogs for generating charts. We'd like to visualize how mean reaction times are related to the order in which people went through the 3 alcohol conditions. Note that you need to have the SPSS Python Essentials properly installed for running these examples on your own computer. We'll use alcotest.sav throughout, part of which is shown below.
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We'll show how to do so on some real world examples. The right way for looping over tables, charts and other procedures in SPSS is with Python. SPSS with Python – Looping over Scatterplots Since Python starts counting at 0, valLab does just that. Precisely, we want characters 9 through last. We adjust it by extracting a substring in Python. We'll first create a string holding just one value label. Removing Characters with Pythonįirst off, you need to have the SPSS Python Essentials properly installed for running this tutorial’s syntax. Fortunately, SPSS with Python allows us to fix the problem with just a few lines of code. Obviously, we don't want to see these value labels in our output but we don't want to adjust all of them manually either. Undesired Prefixes in Value LabelsĬlicking on some value labels in variable view confirms that they've undesired prefixes as shown below. The screenshot below shows part of the data.
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Unfortunately, the software for downloading the data in SPSS format prefixes all variable and value labels with the variable names. A local supermarket held a small survey, the data of which are in minisurvey.sav.