teradataml.store.feature_store.models.DatasetCatalog.list_datasets = list_datasets(self)

DESCRIPTION:
    Lists the available datasets in the repo for the corresponding data domain.
 
PARAMETERS:
    None
 
RETURNS:
    Teradataml DataFrame
 
RAISES:
    TeradataMlException
 
EXAMPLES:
    # Upload features first to create a dataset. 
    >>> from teradataml import load_example_data, FeatureProcess
    >>> load_example_data('dataframe', 'sales')
    >>> df = DataFrame("sales")
 
    # Create a 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
 
    # Run the feature process to ingest features.
    >>> fp = FeatureProcess(repo='vfs_v1', data_domain='sales', object=df, entity='accounts',
    ...                     features=['Jan', 'Feb', 'Mar', 'Apr'])
 
    # Build dataset. 
    >>> from teradataml import DatasetCatalog
    >>> dc = DatasetCatalog(repo='vfs_v1', data_domain='sales')
    >>> dataset = dc.build_dataset(entity='accounts',
    ...                            selected_features = {
    ...                                    'Jan': fp.process_id,
    ...                                    'Feb': fp.process_id},
    ...                            view_name='ds_jan_feb',
    ...                            description='Dataset with Jan and Feb features')
 
    # Example: List datasets. 
    >>> dc.list_datasets()
                                         data_domain        name entity_name                        description                      valid_start                       valid_end
    id                                                                                                                                                                         
    201bb332-dcb3-4fe1-9a7d-d575b36c8790       sales  ds_jan_feb    accounts  Dataset with Jan and Feb features   2025-06-12 12:06:15.572420+00:  9999-12-31 23:59:59.999999+00: