Teradata Package for Python Function Reference | 20.00 - __rmul__ - 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
teradataml.dataframe.sql.DataFrameColumn.__rmul__ = __rmul__(self, other)
Compute the product between two ColumnExpressions using *.
 
PARAMETERS:
    other:
        Required Argument.
        Specifies Python literal or another ColumnExpression.
        Types: ColumnExpression, Python literal
 
RETURNS:
    ColumnExpression
 
RAISES:
    Exception
        A TeradataMlException gets thrown if SQLAlchemy
        throws an exception when evaluating the expression.
 
EXAMPLES:
    # Example 1: Double the income and assign increased
    #            income to new column 'double_income'.
     >>> df.assign(double_income=df.income * 2)
       start_time_column end_time_column  expenditure  income  investment  double_income
    id
    3           67/06/30        07/07/10        434.0   485.0       185.0          970.0
    2           67/06/30        07/07/10        421.0   465.0       179.0          930.0
    1           67/06/30        07/07/10        415.0   451.0       180.0          902.0
    5           67/06/30        07/07/10        459.0   509.0       211.0         1018.0
    4           67/06/30        07/07/10        448.0   493.0       192.0          986.0
 
    # Example 2: Filter out the rows after increasing the income by 10% is greater than 500.
    >>> df[(df.income + df.income * 0.1) > 500]
       start_time_column end_time_column  expenditure  income  investment
    id
    2           67/06/30        07/07/10        421.0   465.0       179.0
    4           67/06/30        07/07/10        448.0   493.0       192.0
    3           67/06/30        07/07/10        434.0   485.0       185.0
    5           67/06/30        07/07/10        459.0   509.0       211.0