Teradata Package for Python Function Reference | 20.00 - __init__ - 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 - 20.00

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
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_Enterprise_2000
lifecycle
latest
Product Category
Teradata Vantage
teradataml.store.feature_store.models.Feature.__init__ = __init__(self, name, column, feature_type=<FeatureType.CONTINUOUS: 1>, description=None, tags=None, status=<FeatureStatus.ACTIVE: 1>)
DESCRIPTION:
    Constructor for Feature.
 
PARAMETERS:
    name:
        Required Argument.
        Specifies the unique name of the Feature.
        Types: str.
 
    column:
        Required Argument.
        Specifies the DataFrame Column.
        Types: teradataml DataFrame Column
 
    feature_type:
        Optional Argument.
        Specifies whether feature is continuous or discrete.
        Default Value: FeatureType.CONTINUOUS
        Types: FeatureType Enum
 
    description:
        Optional Argument.
        Specifies human readable description for Feature.
        Types: str
 
    tags:
        Optional Argument.
        Specifies the tags for Feature.
        Types: str OR list of str
 
    status:
        Optional Argument.
        Specifies whether feature is archived or active.
        Types: FeatureStatus Enum
 
RETURNS:
    None.
 
RAISES:
    None
 
EXAMPLES:
    >>> from teradataml import DataFrame, Feature, FeatureType, load_example_data
    # Load the sales data to Vantage.
    >>> load_example_data("dataframe", "sales")
    # Create DataFrame on sales data.
    >>> df = DataFrame("sales")
    >>> df
    >>> df
                  Feb    Jan    Mar    Apr    datetime
    accounts
    Orange Inc  210.0    NaN    NaN  250.0  04/01/2017
    Jones LLC   200.0  150.0  140.0  180.0  04/01/2017
    Blue Inc     90.0   50.0   95.0  101.0  04/01/2017
    Alpha Co    210.0  200.0  215.0  250.0  04/01/2017
    Yellow Inc   90.0    NaN    NaN    NaN  04/01/2017
 
    # create a Categorical Feature for column 'Feb' for 'sales' DataFrame and name it as
    # 'sales_Feb'.
    >>> from teradataml import Feature
    >>> feature = Feature('sales_Feb', column=df.Feb, feature_type=FeatureType.CATEGORICAL)
    >>> feature
    Feature(name=sales_Feb)
    >>>