SAS to Python guide proc freq proc freq data=mydata; tables myvar / nocol nopercent nocum; run; mydata.myvar.value_counts().sort_index() sort by frequency proc freq order=freq data=mydata; tables myvar / nocol nopercent nocum; run; mydata.myvar.value_counts() with missing proc freq order=freq data=mydata; tables myvar / nocol nopercent nocum missing; run; mydata.myvar.value_counts(dropna=False) proc means proc means data=mydata n mean std min max p25 median p75; var myvar; run; mydata.myvar.describe() more percentiles proc means data=mydata n mean std min max p1 p5 p10 p25 median p75 p90 p95 p99; var myvar; run; mydata.myvar.describe(percentiles=[.01, .05, .1, .25, .5, .75, .9, .95, .99]) data step concatenate datasets data concatenated; set mydata1 mydata2; run; concatenated = pandas.concat([mydata1, mydata2]) proc contents proc contents data=mydata; run; mydata.info() save output proc contents noprint data=mydata out=contents; run; contents = mydata.info() # check this is right Misc number of rows in a datastep * haha nice try. Try this for size: http://www2.sas.com/proceedings/sugi26/p095-26.pdf; len(mydata)