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 explain how to get data by specifying columns using loc / iloc / ix in CASTable.
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
Use loc to specify the column and specify the column name in the second argument.
tbl.loc[:, 'petal_width'].head()
Then you can only get the value of the specified column.
0 2.0
1 2.3
2 2.0
3 2.3
4 2.2
Name: petal_width, dtype: float64
This can be specified in multiple columns.
tbl.loc[:, 'sepal_length':'petal_length'].head()
For multiple columns, the header row is displayed. Since it is specified by the range, the sepal_width that exists between them is also displayed.
| sepal_length | sepal_width | petal_length | |
|---|---|---|---|
| 0 | 7.9 | 3.8 | 6.4 |
| 1 | 7.7 | 2.6 | 6.9 |
| 2 | 7.7 | 2.8 | 6.7 |
| 3 | 7.7 | 3.0 | 6.1 |
| 4 | 7.7 | 3.8 | 6.7 |
If you want to specify the column, specify it as an array.
tbl.loc[:, ['petal_width', 'sepal_width']].head()
| petal_width | sepal_width | |
|---|---|---|
| 0 | 2.0 | 3.8 |
| 1 | 2.3 | 2.6 |
| 2 | 2.0 | 2.8 |
| 3 | 2.3 | 3.0 |
| 4 | 2.2 | 3.8 |
You can specify numbers instead of column names.
tbl.loc[:, 3].head()
0 2.0
1 2.3
2 2.0
3 2.3
4 2.2
Name: petal_width, dtype: float64
The same applies to multiple column specifications.
tbl.iloc[:, 0:3].head()
| sepal_length | sepal_width | petal_length | |
|---|---|---|---|
| 0 | 7.9 | 3.8 | 6.4 |
| 1 | 7.7 | 2.6 | 6.9 |
| 2 | 7.7 | 2.8 | 6.7 |
| 3 | 7.7 | 3.0 | 6.1 |
| 4 | 7.7 | 3.8 | 6.7 |
When specifying a column, the same is true for an array.
tbl.iloc[:, [3, 1]].head()
| petal_width | sepal_width | |
|---|---|---|
| 0 | 2.0 | 3.8 |
| 1 | 2.3 | 2.6 |
| 2 | 2.0 | 2.8 |
| 3 | 2.3 | 3.0 |
| 4 | 2.2 | 3.8 |
Combinations of numbers and column names are also possible.
tbl.ix[:, [3, 'sepal_width']].head()
| petal_width | sepal_width | |
|---|---|---|
| 0 | 2.0 | 3.8 |
| 1 | 2.3 | 2.6 |
| 2 | 2.0 | 2.8 |
| 3 | 2.3 | 3.0 |
| 4 | 2.2 | 3.8 |
There are many situations where you want to analyze only some data. In such a case, use loc, iloc, or ix to quickly extract the data. Numbers are also easy to use for looping, so they should also be useful for automating analytical and computational processing.
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