mepsrajput

title "Simple proc means";

/* Simple proc means  */
PROC MEANS DATA=SASHELP.CARS;
RUN;

title "Select the required variables & drop the labels";
/* Select the variables & drop the labels  */
PROC MEANS DATA=SASHELP.CARS nolabels;
var 

mepsrajput

from IPython.display import display

def multiFreq(dataset, variable_list):
    for i in variable_list:
        datax = dataset[f'{i}'].value_counts()
        datay = pd.DataFrame({
            f'{i}': datax.index, 
            'Frequency': datax.values,
            'Percent': ((datax.values/datax.values.sum())*100).round(2),
            'Cumulative Frequency': datax.values.cumsum(),

mepsrajput

/* freq procedure with multiple variables */
proc freq data=hgrosser;
	tables GENRE MOVIE;
run;

mepsrajput

datax = data['GENRE'].value_counts(dropna=False)
datay = pd.DataFrame({
    'GENRE': datax.index, 
    'Frequency': datax.values,
    'Percent': ((datax.values/datax.values.sum())*100).round(2),
    'Cumulative Frequency': datax.values.cumsum(),
    'Cumulative Percent': ((datax.values.cumsum()/datax.values.sum())*100).round(2)
})

datay

mepsrajput

/* freq procedure with missing */
proc freq data=Gov_C_SAS;
	tables GENRE / missing;
run;

mepsrajput

datab = pd.crosstab(data.county, data.state, margins=True, margins_name='Total')

datab

mepsrajput

proc freq data=Gov_C_SAS;
  tables county*state / norow nocol nopercent;
run;

mepsrajput

datax = data['state'].value_counts().sort_index()
datay = pd.DataFrame({
        'state': datax.index, 
        'Frequency': datax.values
})

datay

mepsrajput

proc freq data = Gov_C_SAS;
  tables state / nopercent nocum;
run;

mepsrajput

datax = data['state'].value_counts()
datay = pd.DataFrame({
  'state': datax.index, 
  'Frequency': datax.values,
  'Percent': ((datax.values/datax.values.sum())*100).round(2),
  'Cumulative Frequenc': datax.values.cumsum(),
  'Cumulative Percen':((datax.values.cumsum()/datax.values.sum())*100).round(2)
})

datay