libname cars '/home/u35108489/prob';
 data work.cars;
 set cars;
 run;

 libname cars '/home/u35108489/prob';
 proc copy in=work out=cars;
 select cars;
run;quit;
proc print data=cars;
run;
/* 1) /
data cars;
    infile '/home/u35108489/prob/cars.txt' dsd;
    input Id First_Name $ Last_Name $ Salary Resident $ CarMark $;
run;

/ 2) /
title "Lista angajatilor cu salariul mai mic de 10";
proc print data = cars;
        where Salary < 10;
        var Id Last_Name First_Name Salary;
run;

/ 3) /

title "Lista proprietarilor care detin Audi cu salariul marit";
data cars1;
set cars;
        where CarMark eq 'Audi';
        Salary = Salary1.1;
proc print data=cars1;
run;


/* 4) */
libname cars '/home/u35108489/prob';
 data work.cars;
 set cars;
 run;

---------------------------------------------------
 libname cars '/home/u35108489/prob';
 proc copy in=work out=cars;
 select cars;
run;quit;
proc print data=cars;
run;
pip install pandas
import pandas as pd

# 1
dataframe = pd.read_csv("angajati4p.csv")
#print(dataframe)
dataframe = dataframe.values

# 2
for line in dataframe:
    if line[3] > 90 and line[5] == "Female":
        print("{} {} {} {} {}".format(line[0], line[1], line[2], line[3], line[5]))
        print(f"{line[0]} {line[1]} {line[2]} {line[3]} {line[5]}")

# 3
for line in dataframe:
    if "Ro" in line[1] or "Ro" in line[2]:
        print("{} {} {}".format(line[0], line[1], line[2]))

# 4
salarii = [x[3] for x in dataframe]
mean_salarii = float(sum(salarii))/len(salarii)
#print(mean_salarii)

new_data = [x for x in dataframe if x[3] > mean_salarii]
new_data = pd.DataFrame(new_data)

new_data.to_csv("output.csv", sep=",", index=False)
dataframe.remove(dataframe[2])
dataframe = np.delete(dataframe, 5,1)
for line in dataframe:
    if line[3] < 10:
        dataframe.remove(line)