teradataml.dataframe.sql.DataFrameColumn.__mul__ = __mul__(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:
    >>> load_example_data("burst", "finance_data")
    >>> df = DataFrame("finance_data")
    >>> df
       start_time_column end_time_column  expenditure  income  investment
    id
    1           67/06/30        07/07/10        415.0   451.0       180.0
    4           67/06/30        07/07/10        448.0   493.0       192.0
    2           67/06/30        07/07/10        421.0   465.0       179.0
    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
 
    # Example 1: Increase the income for each id by 10 % and assign increased
    #            income to new column 'increased_income'.
    >>> df.assign(increased_income=df.income + df.income * 0.1)
       start_time_column end_time_column  expenditure  income  investment  increased_income
    id
    1           67/06/30        07/07/10        415.0   451.0       180.0             496.1
    4           67/06/30        07/07/10        448.0   493.0       192.0             542.3
    2           67/06/30        07/07/10        421.0   465.0       179.0             511.5
    3           67/06/30        07/07/10        434.0   485.0       185.0             533.5
    5           67/06/30        07/07/10        459.0   509.0       211.0             559.9
 
    # 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