Teradata Package for Python Function Reference | 20.00 - list_entities - 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 - 20.00

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
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_Enterprise_2000
lifecycle
latest
Product Category
Teradata Vantage
teradataml.store.feature_store.feature_store.FeatureStore.list_entities = list_entities(self, archived=False) -> teradataml.dataframe.dataframe.DataFrame
DESCRIPTION:
    List all the entities.
 
PARAMETERS:
    archived
        Optional Argument
        Specifies whether to list effective entities or archived entities.
        When set to False, effective entities in FeatureStore are listed,
        otherwise, archived entities 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 Entities in the repo 'vfs_v1'.
    >>> fs.list_entities()
                        description
    name  entity_column
    sales accounts             None
    >>>
 
    # Example 2: List all the archived Entities in the repo 'vfs_v1'.
    # Note: Entity cannot be archived if it is a part of FeatureGroup.
    #       First create another Entity, and update FeatureGroup with
    #       other Entity. Then archive Entity 'sales'.
    >>> entity = Entity('store_sales', columns=df.accounts)
    # Update new entity to FeatureGroup.
    >>> fg.apply(entity)
    # Update FeatureGroup to FeatureStore. This will update Entity
    #    from 'sales' to 'store_sales' for FeatureGroup 'sales'.
    >>> fs.apply(fg)
    True
    # Let's archive Entity 'sales' since it is not part of any FeatureGroup.
    >>> fs.archive_entity('sales')
    Entity 'sales' is archived.
    True
    >>>
 
    # List the archived entities.
    >>> fs.list_entities(archived=True)
        name description               creation_time modified_time               archived_time entity_column
    0  sales        None  2024-10-18 05:41:36.932856          None  2024-10-18 05:50:00.930000      accounts
    >>>