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