Teradata Package for Python Function Reference on VantageCloud Lake - archive_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
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Lake_2000
- Product Category
- Teradata Vantage
- teradataml.store.feature_store.feature_store.FeatureStore.archive_feature_group = archive_feature_group(self, feature_group)
- DESCRIPTION:
Archives FeatureGroup from repository. Note that archived FeatureGroup
is not available for any further processing. Archived FeatureGroup can be
viewed using "list_archived_feature_groups()" method.
Note:
The function archives the associated Features, Entity and DataSource
if they are not associated with any other FeatureGroups.
PARAMETERS:
feature_group:
Required Argument.
Specifies either the name of FeatureGroup or Object of FeatureGroup
to archive from repository.
Types: str OR FeatureGroup
RETURNS:
bool.
RAISES:
TeradataMLException, TypeError, ValueError
EXAMPLES:
>>> from teradataml import DataFrame, FeatureGroup, FeatureStore
>>> load_example_data('dataframe', ['sales'])
# Create teradataml DataFrame.
>>> df = DataFrame("sales")
# Create FeatureGroup from teradataml DataFrame.
>>> fg = FeatureGroup.from_DataFrame(name="sales", entity_columns="accounts", df=df, timestamp_col_name="datetime")
# Create FeatureStore for the repo 'staging_repo'.
>>> fs = FeatureStore("staging_repo")
# Apply FeatureGroup to FeatureStore.
>>> fs.apply(fg)
True
# List all the available FeatureGroups.
>>> fs.list_feature_groups()
description data_source_name entity_name
name
sales None sales sales
# Archive FeatureGroup with name "sales".
>>> fs.archive_feature_group(feature_group='sales')
FeatureGroup 'sales' is archived.
True
>>>
# List all the available FeatureGroups after archive.
>>> fs.list_feature_groups()
Empty DataFrame
Columns: [description, data_source_name, entity_name]
Index: []