archive_features() | FeatureCatalog Method | Teradata Package for Python - archive_features() - Teradata Package for Python

Use the archive_features() method to archive the feature values from the feature catalog.

Required Parameter

features

Specifies names of the features to be archived 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.

List the archived features.

>>> fc.list_features(archived=True)

feature_id name data_type feature_type valid_start valid_end

Example 1: Archive the single feature from feature catalog

>>> fc.archive_features(features='Apr')

True

Validate the features are archived.

>>> fc.list_features(archived=True)

             feature_id name data_type feature_type                     valid_start                       valid_end
entity_name                                                                                                       
accounts              4  Apr    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:10.190000+00:

Example 2: Archive multiple feature values from feature catalog

>>> fc.archive_features(features=['Jan', 'Feb'])

True

Validate the features are archived.

>>> fc.list_features(archived=True)

             feature_id name data_type feature_type                     valid_start                       valid_end
entity_name                                                                                                       
accounts              1  Feb     FLOAT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
accounts              2  Jan    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
accounts              4  Apr    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:10.190000+00: