Lesson 15, Part 3: SAS-R-Python Code Comparisons¶

SAS Code¶

proc format;  
value gender_fmt 1 = 'Male'                   
                 2 = 'Female';  
data mydata;   	
  gender = 1; output;   	
  gender = 2; output;
run; 

proc print data=mydata; 
run;

proc print data=mydata; 
format gender gender_fmt.;  
run;

R Code¶

gender <- c(1,2) 
mydata <- data.frame(gender) print(mydata) 

mydata$gender <- factor(mydata$gender, 
                 levels = c(1,2), 
    			 labels = c("Male", "Female")) 
print(mydata)

Python Code¶

import pandas as pd 
df = pd.DataFrame({'gender':[1,2]})
print(df) 

dic = {1:'Male', 2:'Female'} 
df['gender'] = df['gender'].map(dic) 
print(df)
In [1]:
import os
os.chdir(r"C:\Explore\SAS\Lesson15") 
%pwd
Out[1]:
'c:\\Explore\\SAS\\Lesson15'
In [3]:
import saspy
sas = saspy.SASsession()
sas.submitLST(
"""
proc format;  
value gender_fmt 1 = 'Male'                   
                 2 = 'Female';  
data mydata;   	
  gender = 1; output;  
  gender = 2; output;
run; 
proc print data=mydata; 
format gender gender_fmt.;  
run; 
""")

Using SAS Config named: winlocal
SAS Connection established. Subprocess id is 22404
SAS Output

The SAS System

Obs gender
1 Male
2 Female
In [5]:
import pandas as pd 
df = pd.DataFrame({'gender':[1,2]})
dic = {1:'Male', 2:'Female'} 
df['gender'] = df['gender'].map(dic) 
print(df) 

   gender
0    Male
1  Female
In [ ]: