Search for components in FeatureStore | Teradata Package for Python - Search for components in FeatureStore - Teradata VantageCloud Lake

Lake - Analyze Your Data with ClearScape Analytics™
Deployment
VantageCloud
Edition
Lake
Product
Teradata VantageCloud Lake
Release Number
Published
February 2025
ft:locale
en-US
ft:lastEdition
2026-02-20
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tcl1683670667798.ditamap
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pny1626732985837.ditaval
dita:id
tcl1683670667798

Use the following functions to search for the relevant component in FeatureStore.

list_features

Use list_features() to retrieve available Features in the teradataml DataFrame. You can use any filter that is supported by teradataml DataFrame to search Feature details.

You can use the same API to list down archived Features. See Remove components from FeatureStore.

Example:

>>> fs.list_features()
      column_name description               creation_time modified_time  tags data_type feature_type  status group_name
name
Mar          Mar        None  2024-09-30 11:21:43.314118          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
Jan          Jan        None  2024-09-30 11:21:42.655343          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
Apr          Apr        None  2024-09-30 11:21:44.143402          None  None    BIGINT   CONTINUOUS  ACTIVE      sales
Feb          Feb        None  2024-09-30 11:21:41.542627          None  None     FLOAT   CONTINUOUS  ACTIVE      sales
>>>

list_entities

Use list_entities() to retrieve available Entities in the teradataml DataFrame. You can use any filter that is supported by teradataml DataFrame to search Entity details.

You can use the same API to list down archived Entities. See Remove components from FeatureStore.

Example:

>>> fs.list_entities()
                    description
name  entity_column
sales accounts             None
>>>

list_data_sources

Use list_data_sources() to retrieve available Data Sources in the teradataml DataFrame. You can use any filter that is supported by teradataml DataFrame to search Data Source details.

You can use the same API to list down archived Data Sources. See Remove components from FeatureStore.

Example:

>>> fs.list_data_sources()
            description timestamp_col_name                            source
name
admissions        None               None  select * from "admissions_train"
>>>

list_feature_groups

Use list_feature_groups() to retrieve available Feature Groups in the teradataml DataFrame. You can use any filter that is supported by teradataml DataFrame to search Feature Group details.

You can use the same API to list down archived Feature Groups. See Remove components from FeatureStore.
>>> fs.list_feature_groups()
           description data_source_name entity_name
name
admissions        None       admissions  admissions
>>>