teradataml.store.feature_store.models.FeatureCatalog.upload_features = upload_features(self, object, entity=None, filters=None, features=None, as_of=None, description=None)

DESCRIPTION:
    Uploads the features in to Feature Catalog from the DataFrame.
    Notes:
         * Values in Entity column(s) must be unique.
         * Entity column(s) should not have null values.
         * One can associate a feature with only one entity in a specific
           data domain. Use other data domain if the feature with same name
           is associated with same entity.
 
PARAMETERS:
    object:
        Required Argument.
        Specifies the source to ingest feature values. It can be one of the following:
            * teradataml DataFrame
            * Feature group
            * Process id
        Notes:
             * If "object" is of type teradataml DataFrame, then "entity"
               and "features" should be provided.
             * If "object" is of type str, then it is considered as
               as process id of an existing FeatureProcess and reruns the
               process. Entity and features are taken from the existing
               feature process. Hence, the arguments "entity" and "features"
               are ignored.
             * If "object" is of type FeatureGroup, then entity and features
               are taken from the FeatureGroup. Hence, the arguments "entity"
               and "features" are ignored.
        Types: DataFrame or FeatureGroup or str
 
    entity:
        Optional Argument.
        Specifies Entity for DataFrame.
        Notes:
             * Ignored when "object" is of type FeatureGroup or str.
             * If a string or list of strings is provided, then "object" should
               have these columns in it.
             * If Entity object is provided, then associated columns in Entity
               object should be present in DataFrame.
        Types: Entity or str or list of str
 
    features:
        Optional Argument.
        Specifies list of features to be considered in feature process. Feature
        ingestion takes place only for these features.
        Note:
            * Ignored when "object" is of type FeatureGroup or str.
        Types: Feature or list of Feature or str or list of str.
 
    filters:
        Optional Argument.
        Specifies filters to be applied on data source while ingesting
        feature values for FeatureProcess.
        Types: str or list of str or ColumnExpression or list of ColumnExpression.
 
    as_of:
        Optional Argument.
        Specifies the time period for which feature values are ingested.
        Note:
            * If "as_of" is specified as either string or datetime.datetime,
              then specified value is considered as starting time period and
              ending time period is considered as '31-DEC-9999 23:59:59.999999+00:00'.
        Types: str or datetime.datetime or tuple
 
    description:
        Optional Argument.
        Specifies description for the FeatureProcess.
        Types: str
 
RETURNS:
    FeatureProcess.
 
RAISES:
    TeradataMlException
 
EXAMPLES:
    >>> load_example_data('dataframe', ['sales'])
    >>> df = DataFrame("sales")
 
    # Create FeatureStore repo 'vfs_v1'.
    >>> from teradataml import 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
 
    # Create an instance of FeatureCatalog.
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
 
    # Example 1: Upload features from DataFrame.
    # Before uploading features, let's first look at available features. 
    >>> fc.list_features()
    entity_name feature_id name data_type feature_type valid_start valid_end
                                                                                                            
    >>> fp = fc.upload_features(object=df,
    ...                         entity=["accounts"],
    ...                         features=["Feb", "Jan", "Mar", "Apr"])
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' started.
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' completed.
 
    # Verify the features are uploaded.
    >>> fc.list_features()
                feature_id name data_type feature_type                      valid_start                       valid_end
    entity_name                                                                                                        
    accounts              4  Feb     FLOAT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              6  Apr    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              5  Mar    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts         100002  Jan    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00: 
 
    # Example 2: Upload features from FeatureGroup.
    # Create a FeatureGroup object.
    >>> fg = FeatureGroup.from_DataFrame(name="sales", entity_columns="accounts", df=df)
    
    # Create FeatureCatalog object.
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
    >>> fp = fc.upload_features(object=fg)
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' started.
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' completed.
 
    # Verify the features are uploaded.
    >>> fc.list_features()
                 feature_id name data_type feature_type                     valid_start                       valid_end
    entity_name                                                                                                        
    accounts              4  Feb     FLOAT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              6  Apr    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts              5  Mar    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00:
    accounts         100002  Jan    BIGINT   CONTINUOUS  2025-06-12 05:28:42.916821+00:  9999-12-31 23:59:59.999999+00: 
 
    # Example 3: Upload features through process id.
    # Create FeatureProcess object.
    >>> fp = FeatureProcess(repo='vfs_v1', 
    ...                     data_domain='sales', 
    ...                     object=df, 
    ...                     entity='accounts', 
    ...                     features=['Jan', 'Feb', 'Mar', 'Apr'])
    >>> fp.run()
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' started.
    Process '01c70f05-4067-11f0-9e8a-fb57338c2e68' completed.
 
    # Create FeatureCatalog object.
    >>> fc = FeatureCatalog(repo='vfs_v1', data_domain='sales')
    >>> fp = fc.upload_features(object=fp.process_id)