Teradata Package for Python Function Reference on VantageCloud Lake - archive_feature - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.

Teradata® Package for Python Function Reference on VantageCloud Lake

Deployment
VantageCloud
Edition
Lake
Product
Teradata Package for Python
Release Number
20.00.00.03
Published
December 2024
ft:locale
en-US
ft:lastEdition
2024-12-19
dita:id
TeradataPython_FxRef_Lake_2000
Product Category
Teradata Vantage
teradataml.store.feature_store.feature_store.FeatureStore.archive_feature = archive_feature(self, feature)
DESCRIPTION:
    Archives Feature from repository. Note that archived Feature
    is not available for any further processing. Archived Feature can be
    viewed using "list_archived_features()" method.
 
PARAMETERS:
    feature:
        Required Argument.
        Specifies either the name of Feature or Object of Feature
        to archive from repository.
        Types: str OR Feature
 
RETURNS:
    bool
 
RAISES:
    TeradataMLException, TypeError, ValueError
 
EXAMPLES:
    >>> from teradataml import DataFrame, Feature, FeatureStore
    >>> load_example_data('dataframe', ['sales'])
    # Create teradataml DataFrame.
    >>> df = DataFrame("sales")
    # Create Feature for Column 'Feb'.
    >>> feature = Feature(name="sales_data_Feb", column=df.Feb)
    # Create FeatureStore for the repo 'staging_repo'.
    >>> fs = FeatureStore("staging_repo")
    # Apply the Feature to FeatureStore.
    >>> fs.apply(feature)
    True
    # List the available Features.
    >>> fs.list_features()
                   column_name description               creation_time modified_time  tags data_type feature_type  status group_name
    name
    sales_data_Feb         Feb        None  2024-10-03 18:21:03.720464          None  None     FLOAT   CONTINUOUS  ACTIVE       None
 
    # Archive Feature with name "sales_data_Feb".
    >>> fs.archive_feature(feature=feature)
    Feature 'sales_data_Feb' is archived.
    True
    # List the available Features after archive.
    >>> fs.list_features()
    Empty DataFrame
    Columns: [column_name, description, creation_time, modified_time, tags, data_type, feature_type, status, group_name]
    Index: []
    >>>