Teradata Package for Python Function Reference | 20.00 - mul - 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.mul = mul(self, other)
Compute the multiplication between two ColumnExpressions.
 
PARAMETERS:
    other:
        Required Argument.
        Specifies Python literal or another ColumnExpression.
        Types: ColumnExpression, Python literal
 
RETURNS:
    ColumnExpression
 
EXAMPLES:
    >>> load_example_data("dataframe", "admissions_train")
    >>> df = DataFrame("admissions_train")
    >>> df
       masters   gpa     stats programming  admitted
    id
    13      no  4.00  Advanced      Novice         1
    36      no  3.00  Advanced      Novice         0
    15     yes  4.00  Advanced    Advanced         1
    40     yes  3.95    Novice    Beginner         0
    22     yes  3.46    Novice    Beginner         0
    38     yes  2.65  Advanced    Beginner         1
    26     yes  3.57  Advanced    Advanced         1
    5       no  3.44    Novice      Novice         0
    7      yes  2.33    Novice      Novice         1
    19     yes  1.98  Advanced    Advanced         0
 
    # Example 1: Increase the GPA for each student by 10 % and assign
    #            increased income to new column 'increased_gpa'.
    >>> df.assign(increased_gpa=df.gpa + df.gpa.mul(0.1))
       masters   gpa     stats programming  admitted  increased_gpa
    id
    22     yes  3.46    Novice    Beginner         0          3.806
    26     yes  3.57  Advanced    Advanced         1          3.927
    5       no  3.44    Novice      Novice         0          3.784
    17      no  3.83  Advanced    Advanced         1          4.213
    13      no  4.00  Advanced      Novice         1          4.400
    19     yes  1.98  Advanced    Advanced         0          2.178
    36      no  3.00  Advanced      Novice         0          3.300
    15     yes  4.00  Advanced    Advanced         1          4.400
    34     yes  3.85  Advanced    Beginner         0          4.235
    38     yes  2.65  Advanced    Beginner         1          2.915
 
    >>> 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 2: Calculate the percent of investment done of total income and assign the
    #            final amount to new column 'percentage_investment'.
    >>> df.assign(percentage_investment=(df.investment.mul(100)).div(df.income))
       start_time_column end_time_column  expenditure  income  investment  percentage_investment
    id
    3           67/06/30        07/07/10        434.0   485.0       185.0              38.144330
    2           67/06/30        07/07/10        421.0   465.0       179.0              38.494624
    1           67/06/30        07/07/10        415.0   451.0       180.0              39.911308
    5           67/06/30        07/07/10        459.0   509.0       211.0              41.453831
    4           67/06/30        07/07/10        448.0   493.0       192.0              38.945233
 
    # Example 3: Filter out the rows after doubling income is greater than 1000.
    >>> df[(df.income * 2) > 1000]
       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