Teradata Package for Python Function Reference on VantageCloud Lake - delete_feature_group - 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
20.00.00.08
Published
November 2025
ft:locale
en-US
ft:lastEdition
2025-12-05
dita:id
TeradataPython_FxRef_Lake_2000
Product Category
Teradata Vantage
teradataml.store.feature_store.feature_store.FeatureStore.delete_feature_group = delete_feature_group(self, feature_group)
DESCRIPTION:
    Removes archived FeatureGroup from repository.
    Note:
        Unlike 'archive_feature_group()', this function does not delete the
        associated Features, Entity and DataSource. One should delete those
        using 'delete_feature()', 'delete_entity()' and 'delete_data_source()'.
 
PARAMETERS:
    feature_group:
        Required Argument.
        Specifies either the name of FeatureGroup or Object of FeatureGroup
        to delete from repository.
        Types: str OR FeatureGroup
 
RETURNS:
    bool
 
RAISES:
    TeradataMLException, TypeError, ValueError
 
EXAMPLES:
    >>> from teradataml import DataFrame, FeatureGroup, FeatureStore
    # Create teradataml DataFrame.
    >>> load_example_data('dataframe', ['sales'])
    >>> df = DataFrame("sales")
 
    # Create FeatureStore for repo 'vfs_v1'.
    >>> fs = FeatureStore("vfs_v1", data_domain="d1")
    Repo vfs_v1 does not exist. Run FeatureStore.setup() to create the repo and setup FeatureStore.
    # Setup FeatureStore for this repository.
    >>> fs.setup()
    True
 
    # Example 1: Delete the FeatureGroup 'sales' in the repo 'vfs_v1' using FeatureGroup name.
    # Create FeatureGroup from teradataml DataFrame.
    >>> fg = FeatureGroup.from_DataFrame(name="sales", entity_columns="accounts", df=df, timestamp_column="datetime")
    # Apply FeatureGroup to FeatureStore.
    >>> fs.apply(fg)
    True
 
    # List all the available FeatureGroups.
    >>> fs.list_feature_groups()
                      description data_source_name entity_name               creation_time modified_time
    name  data_domain                                                                                   
    sales d1                 None            sales       sales  2025-07-28 05:00:19.780453          None
 
    # Archive FeatureGroup with name "sales".
    >>> fs.archive_feature_group(feature_group='sales')
    FeatureGroup 'sales' is archived.
    True
 
    # Delete FeatureGroup with name "sales".
    >>> fs.delete_feature_group(feature_group='sales')
    FeatureGroup 'sales' is deleted.
    True
 
    # List all the available FeatureGroups after delete.
    >>> fs.list_feature_groups()
    Empty DataFrame
    Columns: [description, data_source_name, entity_name, creation_time, modified_time]
    Index: []
 
    Example 2: Delete the FeatureGroup 'sales' in the repo 'vfs_v1' using FeatureGroup object.
    # Create FeatureGroup from teradataml DataFrame.
    >>> fg2 = FeatureGroup.from_DataFrame(name="sales", entity_columns="accounts", df=df, timestamp_column="datetime")
    # Apply FeatureGroup to FeatureStore.
    >>> fs.apply(fg2)
    True
 
    # Archive FeatureGroup with FeatureGroup object.
    >>> fs.archive_feature_group(feature_group=fg2)
    FeatureGroup 'sales' is archived.
    True
 
    # Delete FeatureGroup with FeatureGroup object.
    >>> fs.delete_feature_group(feature_group=fg2)
    FeatureGroup 'sales' is deleted.
    True