Teradata Package for Python Function Reference on VantageCloud Lake - list_features - 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.list_features = list_features(self, archived=False) -> teradataml.dataframe.dataframe.DataFrame
DESCRIPTION:
    List all the features.
 
PARAMETERS:
    archived:
        Optional Argument.
        Specifies whether to list effective features or archived features.
        When set to False, effective features in FeatureStore are listed,
        otherwise, archived features are listed.
        Default Value: False
        Types: bool
 
RETURNS:
    teradataml DataFrame
 
RAISES:
    None
 
EXAMPLES:
    >>> from teradataml import DataFrame, FeatureStore, load_example_data
    >>> load_example_data('dataframe', 'sales')
    # Create FeatureStore for repo 'vfs_v1'.
    >>> fs = FeatureStore("vfs_v1")
    # Create teradataml DataFrame.
    >>> df = DataFrame("sales")
    # Create a FeatureGroup from teradataml DataFrame.
    >>> fg = FeatureGroup.from_DataFrame(name='sales',
    ...                                  entity_columns='accounts',
    ...                                  df=df,
    ...                                  timestamp_col_name='datetime')
    # Apply the FeatureGroup to FeatureStore.
    >>> fs.apply(fg)
    True
 
    # Example 1: List all the effective Features in the repo 'vfs_v1'.
    >>> fs.list_features()
         column_name description               creation_time modified_time  tags data_type feature_type  status group_name
    name
    Mar          Mar        None  2024-09-30 11:21:43.314118          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
    Jan          Jan        None  2024-09-30 11:21:42.655343          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
    Apr          Apr        None  2024-09-30 11:21:44.143402          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
    Feb          Feb        None  2024-09-30 11:21:41.542627          None  None     FLOAT   CONTINUOUS  ACTIVE      sales
    >>>
 
    # Example 2: List all the archived Features in the repo 'vfs_v1'.
    # Note: Feature can only be archived when it is not associated with any Group.
    #       Let's remove Feature 'Feb' from FeatureGroup.
    >>> fg.remove(fs.get_feature('Feb'))
    True
    # Apply the modified FeatureGroup to FeatureStore.
    >>> fs.apply(fg)
    True
    # Archive Feature 'Feb'.
    >>> fs.archive_feature('Feb')
    Feature 'Feb' is archived.
    True
 
    # List all the archived Features in the repo 'vfs_v1'.
    >>> fs.list_features(archived=True)
      name column_name description               creation_time modified_time  tags data_type feature_type  status               archived_time group_name
    0  Feb         Feb        None  2024-09-30 11:21:41.542627          None  None     FLOAT   CONTINUOUS  ACTIVE  2024-09-30 11:30:49.160000      sales
    >>>