Quarto Demo with SAS

A simple demo of using SAS in Quarto. In this document we demonstrate the following:

Running SAS code directly
Importing a SAS dataset
Executing SAS commands using saspy
Converting SAS dataset to a Pandas DataFrame
Interactive visualization using observable

Setup

saspy is required to run SAS commands from python/jupyter.

import saspy
import pandas as pd

sas = saspy.SASsession()

Using SAS Config named: oda

SAS Connection established. Subprocess id is 15284

Running SAS code directly from the notebook

The sas.submit() method can be used to submit SAS code directly. The method returns a dictionary with two keys, LOG and LST. The LOG can simply be printed and the LST rendered as HTML using sas.HTML().

ll = sas.submit("""
libname work list;

proc sql;
   select type, count(*) as 'number of models'n, avg(MPG_city) as 'Avg(MPG_City)'n
   from sashelp.cars
   group by type
   order by 3 desc;
quit; 
""")

keys

list of dictionary keys returned by SAS

ll.keys()

dict_keys(['LOG', 'LST'])

LST

LST output

sas.HTML(ll['LST'])

SAS Output

The SAS System


Type	number of models	Avg(MPG_City)
Hybrid	3	55
Wagon	30	21.1
Sedan	262	21.08397
Sports	49	18.40816
Truck	24	16.5
SUV	60	16.1

Alternatively, the LST can be rendered automatically using the sas.submitLST() method

ll = sas.submitLST("""
libname work list;

proc sql;
   select type, count(*) as 'number of models'n, avg(MPG_city) as 'Avg(MPG_City)'n
   from sashelp.cars
   group by type
   order by 3 desc;
quit; 
""")

SAS Output

The SAS System


Type	number of models	Avg(MPG_City)
Hybrid	3	55
Wagon	30	21.1
Sedan	262	21.08397
Sports	49	18.40816
Truck	24	16.5
SUV	60	16.1

Working with SAS and python together

Importing data from SAS

data_sas = sas.sasdata('cars', 'sashelp')

Running SAS PROC MEANS from Quarto

data_sas.means()

	Variable	Label	N	NMiss	Median	Mean	StdDev	Min	P25	P50	P75	Max
0	MSRP	NaN	428.0	0.0	27635.0	32774.855140	19431.716674	10280.0	20329.50	27635.0	39215.0	192465.0
1	Invoice	NaN	428.0	0.0	25294.5	30014.700935	17642.117750	9875.0	18851.00	25294.5	35732.5	173560.0
2	EngineSize	Engine Size (L)	428.0	0.0	3.0	3.196729	1.108595	1.3	2.35	3.0	3.9	8.3
3	Cylinders	NaN	426.0	2.0	6.0	5.807512	1.558443	3.0	4.00	6.0	6.0	12.0
4	Horsepower	NaN	428.0	0.0	210.0	215.885514	71.836032	73.0	165.00	210.0	255.0	500.0
5	MPG_City	MPG (City)	428.0	0.0	19.0	20.060748	5.238218	10.0	17.00	19.0	21.5	60.0
6	MPG_Highway	MPG (Highway)	428.0	0.0	26.0	26.843458	5.741201	12.0	24.00	26.0	29.0	66.0
7	Weight	Weight (LBS)	428.0	0.0	3474.5	3577.953271	758.983215	1850.0	3103.00	3474.5	3978.5	7190.0
8	Wheelbase	Wheelbase (IN)	428.0	0.0	107.0	108.154206	8.311813	89.0	103.00	107.0	112.0	144.0
9	Length	Length (IN)	428.0	0.0	187.0	186.362150	14.357991	143.0	178.00	187.0	194.0	238.0

A simple bar chart (from SAS)

data_sas.bar('EngineSize')

SAS Output

Converting the data to a Pandas DataFrame

data_pd = data_sas.to_df()
data_pd.describe()

	MSRP	Invoice	EngineSize	Cylinders	Horsepower	MPG_City	MPG_Highway	Weight	Wheelbase	Length
count	428.000000	428.000000	428.000000	426.000000	428.000000	428.000000	428.000000	428.000000	428.000000	428.000000
mean	32774.855140	30014.700935	3.196729	5.807512	215.885514	20.060748	26.843458	3577.953271	108.154206	186.362150
std	19431.716674	17642.117750	1.108595	1.558443	71.836032	5.238218	5.741201	758.983215	8.311813	14.357991
min	10280.000000	9875.000000	1.300000	3.000000	73.000000	10.000000	12.000000	1850.000000	89.000000	143.000000
25%	20334.250000	18866.000000	2.375000	4.000000	165.000000	17.000000	24.000000	3104.000000	103.000000	178.000000
50%	27635.000000	25294.500000	3.000000	6.000000	210.000000	19.000000	26.000000	3474.500000	107.000000	187.000000
75%	39205.000000	35710.250000	3.900000	6.000000	255.000000	21.250000	29.000000	3977.750000	112.000000	194.000000
max	192465.000000	173560.000000	8.300000	12.000000	500.000000	60.000000	66.000000	7190.000000	144.000000	238.000000

Interactive visualization with observable

SAS -> python -> observable
Now that our data is available in python we can use the ojs_define function to make it available to observable.

# make data available to observable
ojs_define(ojs_data = data_pd)

The observabale code to generate the plot below is included as folded code.

Code

viewof MPG_City = Inputs.range(
  [10, 60], 
  {value: 10, step: 1, label: "MPG City"}
)

// filter data based on the value based on the MPG_City input
filtered_data = transpose(ojs_data).filter(function(car) {
  return MPG_City < car.MPG_City;
})

Code

plt = Plot.plot({
    marks: [
        Plot.dot(filtered_data, {x: "EngineSize", y: "Horsepower"})
    ]
})