Use the list_features() method to list the available features in the repository for the corresponding data domain.
Optional Parameter
- archived
- Specifies whether to retrieve archived features or not from the feature catalog. When set to True, returns only archived features, Otherwise, returns active features from the feature catalog.
Example setup
>>> load_example_data('dataframe', ['sales'])
>>> df = DataFrame("sales")
Create the feature store repository 'vfs_v1'.
>>> from teradataml import FeatureStore >>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')
Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
Set up the feature store for this repository.
>>> fs.setup()
True
Create an instance of FeatureCatalog.
>>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
Upload features from the DataFrame.
>>> fp = fc.upload_features(object=df, ... entity=["accounts"], ... features=["Feb", "Jan", "Mar", "Apr"])
Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' started. Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' completed.
Example: List features in the repository
List the features.
>>> fc.list_features()
feature_id name data_type feature_type valid_start valid_end entity_name accounts 4 Feb FLOAT CONTINUOUS 2025-06-12 05:28:42.916821+00: 9999-12-31 23:59:59.999999+00: accounts 6 Apr BIGINT CONTINUOUS 2025-06-12 05:28:42.916821+00: 9999-12-31 23:59:59.999999+00: accounts 5 Mar BIGINT CONTINUOUS 2025-06-12 05:28:42.916821+00: 9999-12-31 23:59:59.999999+00: accounts 100002 Jan BIGINT CONTINUOUS 2025-06-12 05:28:42.916821+00: 9999-12-31 23:59:59.999999+00: