Example 1: Ingest the feature values using DataFrame 'df' to the repo "vfs_test" - Teradata Package for Python

Example setup

>>> load_example_data('dataframe', ['sales'])
>>> df = DataFrame("sales")

Create a feature store.

>>> from teradataml import FeatureStore
>>> fs = FeatureStore("vfs_test", data_domain='sales')

Repo vfs_test does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.

>>> fs.setup()

True

Create a feature process using a DataFrame as the source

>>> fp = FeatureProcess(repo="vfs_test",
...                     data_domain='sales',
...                     object=df,
...                     entity="accounts",
...                     features=["Jan", "Feb", "Mar", "Apr"])

>>> fp.run()

Process '76049397-6b8e-11f0-b77a-f020ffe7fe09' started.
Process '76049397-6b8e-11f0-b77a-f020ffe7fe09' completed.
True

Verify the feature process was recorded

>>> fs.list_feature_processes()

                                     description data_domain       process_type  data_source    entity_id       feature_names feature_ids                     valid_start                       valid_end
process_id                                                                                                                                                                                              
a5de0230-6b8e-11f0-ae70-f020ffe7fe09                   sales      feature group  sales_group  sales_group  Apr, Feb, Jan, Mar        None  2025-07-28 08:41:42.460000+00:  9999-12-31 23:59:59.999999+00:
76049397-6b8e-11f0-b77a-f020ffe7fe09                   sales  denormalized view      "sales"     accounts  Apr, Feb, Jan, Mar        None  2025-07-28 08:40:17.600000+00:  9999-12-31 23:59:59.999999+00: