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

DESCRIPTION:
    Lists the available features for the corresponding dataset.
 
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'])
    >>> from teradataml import DatasetCatalog
 
    # Build dataset.
    >>> 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 features.
    >>> dc.list_features()
                                         data_domain  feature_id feature_name feature_view
    dataset_id                                                                            
    201bb332-dcb3-4fe1-9a7d-d575b36c8790       sales      100001          Feb   ds_jan_feb
    201bb332-dcb3-4fe1-9a7d-d575b36c8790       sales           1          Jan   ds_jan_feb