The teradatamlspk API TeradataSession exposes an API createDataFrame which takes input as table name and returns teradatamlspk DataFrame. With the APIs, you can create teradatamlspk DataFrame for data resides in a Vantage table or view.
See following steps. This example assumes the table 'admissions_train' is already loaded with data.
>>> from teradatamlspk.sql import TeradataSession
>>> tdmkspk = TeradataSession.builder.getOrCreate(host=host, user = user, password=password)
>>> df = tdmlspk.createDataFrame("admissions_train")
>>> df.show()
+--+-------+----+--------+-----------+--------+ |id|masters|gpa | stats |programming|admitted| +--+-------+----+--------+-----------+--------+ |15| yes |4.0 |Advanced| Advanced | 1 | |14| yes |3.45|Advanced| Advanced | 0 | |31| yes |3.5 |Advanced| Beginner | 1 | |40| yes |3.95| Novice | Beginner | 0 | |22| yes |3.46| Novice | Beginner | 0 | |39| yes |3.75|Advanced| Beginner | 0 | |37| no |3.52| Novice | Novice | 1 | |35| no |3.68| Novice | Beginner | 1 | |38| yes |2.65|Advanced| Beginner | 1 | |26| yes |3.57|Advanced| Advanced | 1 | |5 | no |3.44| Novice | Novice | 0 | |3 | no |3.7 | Novice | Beginner | 1 | |1 | yes |3.95|Beginner| Beginner | 0 | |17| no |3.83|Advanced| Advanced | 1 | |34| yes |3.85|Advanced| Beginner | 0 | |13| no |4.0 |Advanced| Novice | 1 | |32| yes |3.46|Advanced| Beginner | 0 | |11| no |3.13|Advanced| Advanced | 1 | |9 | no |3.82|Advanced| Advanced | 1 | |24| no |1.87|Advanced| Novice | 1 | +--+-------+----+--------+-----------+--------+ only showing top 20 rows