Teradata Package for Python Function Reference | 20.00 - Teradata Package for Python Function Reference - 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
Published
March 2024
Language
English (United States)
Last Update
2024-04-10
dita:id
TeradataPython_FxRef_Enterprise_2000
Product Category
Teradata Vantage

This document provides detailed description and complete usage information for all the functions in the Teradata® Package for Python, teradataml.

teradataml Function Categories

Categories of teradataml package functions:

  • <name>: Analytic functions available to use as class. Executing each analytical function means, generating instance of the analytic function class.
  • DataFrame.<name>: Create a teradataml DataFrame for the underlying Teradata table.
  • <name>_context: API functions that manage the connection and certain internal data structures called context.
  • <TDMLDF>.<name>: teradataml DataFrame methods available for data manipulation, preparation and exploration.

Notes:

  • name is part of a function name that indicates the specific task of the function.
  • TDMLDF is teradataml DataFrame object.

teradataml Analytic Function Default Execution Locations

The teradataml analytics package includes two subpackages:

  • teradataml.analytics.mle
    Analytic functions in the teradataml.analytics.mle subpackage require your system to have the Vantage Machine Learning Engine, which is a separate machine learning legacy engine that is not part of the current standard Vantage offer. If your Vantage system does not have the required ML Engine, an error or no-op behavior will occur when functions in this subpackage are invoked.
  • teradataml.analytics.sqle

All teradataml analytic functions are in either of these two subpackages.

For Vantage 1.0 the following nine teradataml analytic functions were executed by default in the Advanced SQL Engine:

  • Attribution
  • DecisionForestPredict
  • DecisionTreePredict
  • GLMPredict
  • NaiveBayesPredict
  • NaiveBayesTextClassifierPredict
  • NPath
  • Sessionize
  • SVMSparsePredict

For Vantage 1.1 or later versions, six new functions are added to the list. The following 15 teradataml analytic functions are executed by default in the Advanced SQL Engine:

  • Antiselect
  • Attribution
  • DecisionForestPredict
  • DecisionTreePredict
  • GLMPredict
  • MovingAverage
  • NaiveBayesPredict
  • NaiveBayesTextClassifierPredict
  • NGramSplitter
  • NPath
  • Pack
  • Sessionize
  • StringSimilarity
  • SVMSparsePredict
  • Unpack

All other teradataml analytic functions are executed by default in the Teradata Machine Learning Engine.

Notes:

  • Teradata recommends importing an analytic function in one of the following ways:

    • Preferred: Import from the teradataml package.
    • To choose the engine where the analytic function is used: Import from the subpackage.
  • The teradataml package includes a module (load_example_data) with datasets for the examples in analytic functions. To execute these examples, you need the following:

    • To have a connection to Teradata Vantage
    • Import the load_example_data module to load the data, using the command:
      from teradataml.data.load_example_data import load_example_data