teradataml.store.feature_store.models.FeatureCatalog.delete_features = delete_features(self, features)

DESCRIPTION:
    Deletes the archived feature values from feature catalog.
    Note:
        * After deleting the feature values from feature catalog table,
          the function also drops the feature table from the repo if
          the feature table is not used by any other feature.
 
PARAMETERS:
    features:
        Required Argument.
        Specifies name of the feature(s) to be deleted from feature catalog.
        Types: str or list of str.
 
RETURNS:
    bool
 
RAISES:
    TeradataMlException
 
EXAMPLES:
    # Create FeatureStore repo 'vfs_v1'.
    >>> from teradataml import FeatureStore
    >>> fs = FeatureStore(repo='vfs_v1', data_domain='sales')
    Repo vfs_test does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    >>> fs.setup()
    True
 
    # Load example data.
    >>> load_example_data('dataframe', ['sales'])
    >>> df = DataFrame("sales")
 
    # Create an instance of FeatureCatalog.
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
 
    # Upload features from 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 features.
    >>> fc.list_features()
                feature_id name data_type feature_type                      valid_start                        valid_end
    entity_name
    accounts              1  Feb     FLOAT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              4  Apr    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              3  Mar    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              2  Jan    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
 
    # Example 1: Delete the single feature value from feature catalog.
    # Before deleting, let's archive the feature values.
    >>> fc.archive_features(features='Apr')
    True
    >>> fc.delete_features(features='Apr')
    True
 
    # Validate the feature is deleted.
    >>> fc.list_features()
                feature_id name data_type feature_type                      valid_start                        valid_end
    entity_name                                                                                                        
    accounts              3  Mar    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  9999-12-31 23:59:59.999999+00:
    accounts              2  Jan    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
    accounts              1  Feb     FLOAT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  2025-06-17 15:27:59.360000+00:
 
    # Example 2: Delete multiple feature values from feature catalog.
    >>> fc.archive_features(features=['Jan', 'Feb'])
    True
    >>> fc.delete_features(features=['Jan', 'Feb'])
    True
 
    # Validate the feature values are deleted.
    >>> fc.list_features()
                 feature_id name data_type feature_type                     valid_start                       valid_end
    entity_name                                                                                                        
    accounts              3  Mar    BIGINT   CONTINUOUS  2025-06-17 15:17:25.057869+00:  9999-12-31 23:59:59.999999+00: