Advanced topics

In this chapter we will explore more detailed explanations of specific functionality.

Using Batch mode

Batch mode is meant to be used when you want to automate your code as Python scripts.

In batch mode, any method that would normally display results, returns a Python dictionary instead and with two keys; LOG, LST. This is the same as how the submit() method works normally.

The LOG has the SAS Log and the LST contains the results. You will likely want to set the results parameter to HTML (this was originally the default instead of Pandas). When you set the results to HTML, not only are plots and graphs in HTML, but also tabular results too.

The example below shows the contents of a Python script that runs a linear regression and writes all the results to a directory. You can access the directory with a web browser to view these results by clicking on them. Adjust the filesystem path below and you should be able to run this code yourself.

#! /usr/bin/python3.5

import saspy
sas = saspy.SASsession(results='html')

cars = sas.sasdata('cars', libref='sashelp')


stat = sas.sasstat()
res = stat.reg(model='horsepower = Cylinders EngineSize', data=cars)

for i in range(len(res._names)):
    x = res.__getattr__(res._names[i])
    if type(x) is not str:
        out1 = open("C:\\Public\\saspy_demo\\"+res._names[i]+".html", mode='w+b')
        out1 = open("C:\\Public\\saspy_demo\\"+res._names[i]+".log", mode='w+b')

The URL to see these results is: file:///C:/Public/saspy_demo/. Of course, you can imagine integrating the results into nicer web page for reporting, but with nothing more than this few lines of code, you can have the results updated and refreshed by just re-running the script.


There are two types of prompting that can be performed; meaning to stop processing and prompt the user for input and then resume processing.

The first type of prompting is performed implicity. When you run the SASsession() method, if any required parameters for the chosen connection method were not specified in the configuration definition (in, processing is interrupted so that the user can be prompted for the missing parameters. In addition, when there is more than one configuration definition in SAS_config_names, and cfgname is not specified in the SASsession() method (or an invalid name is specified), the user will be prompted to select the configuration definition to use.

The other kind of prompting is prompting that you control. The submit() method, and the saslib() methods both take an optional prompt parameter. This parameter is how you request to have the user prompted for input at run time. This option is used in conjunction with SAS macro variable names that you enter in the SAS code or options for the method.

The prompt parameter takes a Python dictionary. The keys are the SAS macro variable names and the values are True or False. The Boolean value indicates whether it is to hide what the user types in or not. It also controls whether the macro variables stay available to the SAS session or if they are deleted after running that code.

You will be prompted for the values of your keys, and those values will be assigned to the SAS macro variables for you in SAS. When your code runs, the macro variables will be resolved. If you specified True, then the value the user types is not displayed, nor is the macro variable displayed in the SAS log, and the macro variable is deleted from SAS so that it is not accessible after that code submission. For False, the user can see the value as it is type, the macro variables can be seen in the SAS log and the variables remain available in that SAS session for later code submissions.

The following are examples of how to use prompting in your programs. The first example uses the saslib() method to assign a libref to a third-party database. This is a common issue–the user needs to specify credentials, but you do not want to include user IDs and passwords in your programs. Prompting enables the user to provide credentials at runtime.

sas.saslib('Tera', engine='Teradata', options='user=&user pw=&mypw server=teracop1',
           prompt={'user': False, 'mypw': True})

At runtime, the user is prompted for user and password and sees something like the following when entering values (the user ID is visible and the password is obscured):

Please enter value for macro variable user sasdemo
Please enter value for macro variable mypw ........

Another example might be that you have code that creates a table, but you want to let the user choose the table name as well as the name of the column and a hidden value to assign to it. By specifing False, the user can see the value, and the SAS log shows the non-hidden marco mariables, followed by another code submission that uses the previously defined non-hidden variables–which are still available.

ll = sas.submit('''
data &dsname;
  do &var1="&pw";
''', prompt={'var1': False, 'pw': True, 'dsname': False})
Please enter value for macro variable var1 MyColumnName
Please enter value for macro variable hidden ........
Please enter value for macro variable dsname TestTable1


103  ods listing close;ods html5 (id=saspy_internal) file=stdout options(bitmap_mode='inline') device=svg; ods graphics on /
103! outputfmt=png;
105  options nosource nonotes;
108  %let var1=MyColumnName;
109  %let dsname=TestTable1;
111  data &dsname;
112    do &var1="&hidden";
113      output;
114    end;
115  run;
NOTE: The data set WORK.TESTTABLE1 has 1 observations and 1 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds

117  proc print data=&dsname;
118  run;
NOTE: There were 1 observations read from the data set WORK.TESTTABLE1.
NOTE: PROCEDURE PRINT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds

120  options nosource nonotes;
124  ods html5 (id=saspy_internal) close;ods listing;

proc print data=&dsname;

Obs     MyColumnName
1       cant see me

That is a highly contrived example, but you get the idea. You can prompt users at runtime for values you want to use in the code, and those values can be kept around and used later in the code, or hidden and inaccessible afterward.

Moving values between Python Variables and SAS Macro Variables

There are two methods on the SASsession object you can use to transfer values between Python and SAS. symget() and symput(). To get a value from a SAS Macro Variable and assign it to a Python variable you just call symget with the name of the macro variable.

py_var = sas.symget(sas_macro_var)

To set the value of a SAS Macro Variable using the value of a Python variable, use symput() specifying the macro variable name, and providing the python variable continaing the value.

py_var = some_value
sas.symput(sas_macro_var, py_var)

Slow performance loading SAS data into a Pandas DataFrame ( to_df(), sd2df() )

Transferring data from SAS into Python (and the reverse) has been in this module from the beginning. As usage of this has grown, larger sized data sets have been shown to be much slower to load and consume lots of memory. After investigations, this has to do with trying to build out the dataframes ‘in memory’. This works fine up to a point, but the memory consumption and CPU usage doesn’t scale.

I’ve made enhancements to the algorithm, so it will work, as opposed to run indefinitely consuming too many resources, but it is still too slow.

So, I’ve added a second method for doing this, using a CSV file as an intermediate store, then using the Pandas read_csv() method to create the dataframe. This performs significantly faster, as it doesn’t consume memory for storing the data in python objects. The read_csv() method is much faster than trying to append data in memory as it’s streamed into python from SAS.

There is now a parameter for these methods to specify which method to use: method=[‘MEMORY’ | ‘CSV’]. The default is still MEMORY. But you can specify CSV to use this new method: to_df(method=’CSV’), sd2df(method=’CSV’).

There are also alias routines which specify this for you: to_df_csv() and sd2df_csv().

Another optimization with this is when saspy and SAS are on the same machine. When this is the case, there is no transfer required. The CSV file written by SAS is the file specified in read_csv(). For remote connections, the CSV file still needs to be transferred from SAS to saspy and written to disk locally for the read_csv() method. This is still significantly faster for larger data.