Check the data summary in CASTable

SAS Viya is an AI platform. It is available through languages such as Python, Java and R. A table object called CASTable is used in SAS Viya (CAS stands for Cloud Analytic Services). This time, I will introduce how to change the extraction conditions when viewing the data status in CASTable.

Get a table from the database

First, connect to SAS Viya.

import swat
conn = swat.CAS('server-name.mycompany.com', 5570, 'username', 'password')

Then get the CASTable. This time, I will use CSV of IRIS data.

tbl = conn.loadtable('data/iris.csv', caslib='casuser').casTable

Check the information

Use the describe method to see what data you have.

tbl.describe()

The result will be returned as follows. You can see the number of rows, standard deviation, minimum value, maximum value, and data worth 25% / 50% / 75%.

sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667
std 0.828066 0.433594 1.764420
min 4.300000 2.000000 1.000000
25% 5.100000 2.800000 1.600000
50% 5.800000 3.000000 4.350000
75% 6.400000 3.300000 5.100000
max 7.900000 4.400000 6.900000

Change the percentage

Changing the percentiles will change the data retrieved. The following is an example of changing to 30% and 80% data.

tbl.describe(percentiles=[0.3, 0.8])
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667
std 0.828066 0.433594 1.764420
min 4.300000 2.000000 1.000000
30% 5.250000 2.800000 1.700000
50% 5.800000 3.000000 4.350000
80% 6.550000 3.400000 5.350000
max 7.900000 4.400000 6.900000

See the whole

Specify ʻinclude ='all'` to check all data.

tbl.describe(include='all')
sepal_length sepal_width petal_length petal_width species
count 150 150 150 150
unique 35 23 43 22
top 5 3 1.5 0.2
freq 10 26 14 28
mean 5.84333 3.054 3.75867 1.19867
std 0.828066 0.433594 1.76442 0.763161
min 4.3 2 1 0.1
25% 5.1 2.8 1.6 0.3
50% 5.8 3 4.35 1.3
75% 6.4 3.3 5.1 1.8
max 7.9 4.4 6.9 2.5

The number can also be a floating point number.

tbl.describe(stats='all')
sepal_length sepal_width petal_length petal_width
count 1.500000e+02 1.500000e+02 1.500000e+02
unique 3.500000e+01 2.300000e+01 4.300000e+01
mean 5.843333e+00 3.054000e+00 3.758667e+00
std 8.280661e-01 4.335943e-01 1.764420e+00
min 4.300000e+00 2.000000e+00 1.000000e+00
25% 5.100000e+00 2.800000e+00 1.600000e+00
50% 5.800000e+00 3.000000e+00 4.350000e+00
75% 6.400000e+00 3.300000e+00 5.100000e+00
max 7.900000e+00 4.400000e+00 6.900000e+00
nmiss 0.000000e+00 0.000000e+00 0.000000e+00
sum 8.765000e+02 4.581000e+02 5.638000e+02
stderr 6.761132e-02 3.540283e-02 1.440643e-01
var 6.856935e-01 1.880040e-01 3.113179e+00
uss 5.223850e+03 1.427050e+03 2.583000e+03
cv 1.417113e+01 1.419759e+01 4.694272e+01
tvalue 8.642537e+01 8.626430e+01 2.609020e+01
probt 3.331256e-129 4.374977e-129 1.994305e-57

Summary

You can use the describe method to get an overview of the data in the CASTable. Please use it as a base for data analysis.

SAS for Developers | SAS

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