Teradata Package for Python Function Reference on VantageCloud Lake - 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 on VantageCloud Lake
- Deployment
- VantageCloud
- Edition
- Lake
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Lake_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