teradataml.store.feature_store.feature_store.FeatureStore.archive_feature_group = archive_feature_group(self, feature_group)

DESCRIPTION:
    Archives FeatureGroup from repository. Note that archived FeatureGroup
    is not available for any further processing. Archived FeatureGroup can be
    viewed using "list_feature_groups(archived=True)" method.
    Note:
        The function archives the associated Features, Entity and DataSource
        if they are not associated with any other FeatureGroups.
 
PARAMETERS:
    feature_group:
        Required Argument.
        Specifies either the name of FeatureGroup or Object of FeatureGroup
        to archive from repository.
        Types: str OR FeatureGroup
 
RETURNS:
    bool.
 
RAISES:
    TeradataMLException, TypeError, ValueError
 
EXAMPLES:
    >>> from teradataml import DataFrame, FeatureGroup, FeatureStore
    # Create teradataml DataFrame.
    >>> load_example_data('dataframe', ['sales'])
    >>> df = DataFrame("sales")
 
    # Create FeatureStore for repo 'vfs_v1'.
    >>> fs = FeatureStore("vfs_v1", data_domain="d1")
    Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    # Setup FeatureStore for this repository.
    >>> fs.setup()
    True
 
    # Example 1: Archive the FeatureGroup 'sales' in the repo 'vfs_v1' using FeatureGroup name.
    # Create FeatureGroup from teradataml DataFrame.
    >>> fg = FeatureGroup.from_DataFrame(name="sales", entity_columns="accounts", df=df, timestamp_column="datetime")
    # Apply FeatureGroup to FeatureStore.
    >>> fs.apply(fg)
    True
 
    # List all the available FeatureGroups.
    >>> fs.list_feature_groups()
                      description data_source_name entity_name               creation_time modified_time
    name  data_domain                                                                                   
    sales d1                 None            sales       sales  2025-07-28 05:00:19.780453          None
 
    # Archive FeatureGroup with name "sales".
    >>> fs.archive_feature_group(feature_group='sales')
    FeatureGroup 'sales' is archived.
    True
 
    # List all the available FeatureGroups after archive.
    >>> fs.list_feature_groups(archived=True)
        name data_domain description data_source_name entity_name               creation_time modified_time               archived_time
    0  sales          d1        None            sales       sales  2025-07-28 05:00:19.780453          None  2025-07-28 05:02:04.100000
 
    # Example 2: Archive the FeatureGroup 'sales' in the repo 'vfs_v1' using FeatureGroup object.
    # Create FeatureGroup from teradataml DataFrame.
    >>> fg2 = FeatureGroup.from_DataFrame(name="sales_df", entity_columns="accounts", df=df, timestamp_column="datetime")
    # Apply FeatureGroup to FeatureStore.
    >>> fs.apply(fg2)
    True
 
    # Archive FeatureGroup with FeatureGroup object.
    >>> fs.archive_feature_group(feature_group=fg2)
    FeatureGroup 'sales_df' is archived.
    True
 
    # List all the available FeatureGroups after archive.
    >>> fs.list_feature_groups(archived=True)
        name data_domain description data_source_name entity_name               creation_time modified_time               archived_time
    0  sales          d1        None            sales       sales  2025-07-28 05:00:19.780453          None  2025-07-28 05:02:04.100000
    1  sales_df       d1        None            sales       sales  2025-07-28 05:02:01.123456          None  2025-07-28 05:03:35.456789