Work in Progress.

Purpose	SAS	Python	R
input	PROC IMPORT DATAFILE = “ “ OUT=df DBMS=CSV REPLACE; GUESSINGROWS=10000; RUN;	pd.read_csv(“”) # encoding=”cp1252”,encoding=”ISO-8859-1”	read.table(file, as.is=TRUE)
		pd.read_sas(““,encoding=”latin-1” )	read.csv(“”, header=TRUE)
			load() # load data written with save
output	PROC EXPORT DATA= df OUTFILE= “” DBMS=csv REPLACE; RUN;	df.to_csv(index=False)	save()
content	proc contents data = df	array.ndim, .shape, .size, .dtype, .itemsize, .nbytes	str(df)
	out = dsList (keep=memname memlabel name label nobs varnum) noprint;run;
		df.info(), df.dtypes
summary	PROC MEANS DATA=df NWAY; CLASS species; VAR x1-x6; RUN;	df.describe()	summary(dt)
	PROC SUMMARY	df.x.describe()
	PROC SQL	pd.pivot_table()
		df.groupby(by=’x’).sum()
		df.groupby(by=’x’).count()
		df.groupby(by=’x’).quantile([0.25,0.75])
		df.groupby(level=’ind1’)
missing count	PROC MEANS N NMISS MISSING;	df.count()
		df.isnull().sum()
	PROC FREQ; TABLE /MISSING;
frequency	PROC FREQ;TABLE value;	df.describe()
		df.value.counts ()		PROC SQL; SELECT COUNT(X) AS CT GROUPBY value;
		pd.crosstab(df.A, df.B).apply(lambda x: x/x.sum(), axis = 1)
distribution	PROC UNIVARIATE	df.describe(include=[np.number])
drop/keep columns	DATA df (drop = col_name);	df.drop([‘x1’, ‘x2’, ‘x3’], axis = 1
	DATA df (keep = col_name);	df.loc[:, [‘x1’, ‘x2’])
rename	DATA df (RENAME = (old=new col_name));	df.columns = [‘name1’, ‘name2’, ‘name3’]
sort	PROC SORT; BY x1 DESCENDING x2;	df.sort([‘x1’, ‘x2’], ascending = [True, False])
binning	PROC RANK;	pd.cut(x, [min, cut1, …, cutk, max])
		np.digitize(x, [cut1, cut2, …, cutk])
		pd.qcut(df.x, n, labels=False)
replace value	IF THEN;	df.x.replace(zip(old,new))
combine datasets	DATA + MERGE;	pd.merge(df1, df2, how=’left’,on=’x’)
	PROC SQL;
filter join	DATA + in;	df1[df1.x.isin(df2.x)]
	PROC SQL	df1[~df1.x.isin(df2.x)]
get help		object ?	?object
upgrade library		pip3 install –upgrade packageName –user
directory	Filename filelist pipe “dir /b /s c:\temp*.sas”;	import os	dir()
	Data null; Infile filelist truncover;	os.listdir(“”)
	Input filename $100.;
	Put filename=;Run;
working directory		getcwd()	getwd()
change directory		chdir())	setwd()

Custom Snippets

Using custom snippets helps save time. Using 3 languages we have a lot of syntax and libraries to remember.
how to add a snippet.

shift + command + p and type snippets => Select Preferences: Open User Snippets snippet generator

Run external code

If we have code that we use again and again, let us keep it in a separate piece of code (Calling it a piece of code is a genearal way of saying it, which may have other names such as “module”, “function”, or “macro” in SAS).

Try not to copy and paste even if “Ctrl C” and “Ctrl V” may be our favorite technology. Why? Because copying and pasting code all over the place can make our code much longer (and dreadful sometimes) than needed, and messy.

Python - 2 ways depending on which enviroment I am using. I often use both Jupyter Notebook and the command line simultaneously.

%run magic command in Jupyter Notebook. E.g. %run C:/…/myCode.py
python C:/…/myCode.py in command prompt.

Note: Imported libraries are cached. So you import an updated version of the library, it will still be the old one showing up, unless you start a new session.

Note: NEVER from libraryName import * It can cause name clashes and all kinds of mysterious bad stuff.

Import and run a piece of external code in SAS is easy. Say we have a few lines of code contained in “step0_libnames_options.sas” that specifies options, directories and a few macro variables for our project. We can call it to task by using the %include statement.

codestep0_libnames_options.sas

options mprint mlogic symbolgen compress=binary;
options varlidvarname=any;
libname newdata "c:\users\sc\newdata";
%let outpath = A:\sc\output;

There are many ways to use the %include statement. Below is a simple example. Remember that what we are calling needs to be in quotes.

codeimport external code.sas

%let code_dir = "c:\users\sc\code";
%include "&code_dir.\step0_libnames_options.sas";

codeimport libraries.r

install.packages('data.table') #data.table has no dependencies
library(data.table)

install.packages('zoo', dependencies = TRUE)
library(zoo)

Python R SAS Data Analysis Lookup

summary

Custom Snippets

Run external code