Teradata Package for Python Function Reference | 20.00 - sub - 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.sub = sub(self, other)
Compute the subtraction between two ColumnExpressions.
 
PARAMETERS:
    other:
        Required Argument.
        Specifies Python literal or another ColumnExpression.
        Types: ColumnExpression, Python literal
 
RETURNS:
    ColumnExpression
 
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: Subtract 100 from the income amount and assign the final amount
    #            to new column 'remaining_income'.
    >>> df.assign(remaining_income=df.income - 100)
       start_time_column end_time_column  expenditure  income  investment  remaining_income
    id
    3           67/06/30        07/07/10        434.0   485.0       185.0             385.0
    2           67/06/30        07/07/10        421.0   465.0       179.0             365.0
    1           67/06/30        07/07/10        415.0   451.0       180.0             351.0
    5           67/06/30        07/07/10        459.0   509.0       211.0             409.0
    4           67/06/30        07/07/10        448.0   493.0       192.0             393.0
 
    # Example 2: Filter the rows where the income left after the investment is more than 300.
    >>> df[df.income.sub(df.investment) > 300]
       start_time_column end_time_column  expenditure  income  investment
    id
    4           67/06/30        07/07/10        448.0   493.0       192.0