Teradata Package for Python Function Reference | 20.00 - regr_sxy - 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
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- Product Category
- Teradata Vantage
- teradataml.dataframe.dataframe.DataFrame.regr_sxy = regr_sxy(expression)
- DESCRIPTION:
Function returns the column-wise sum of the products of the independent variable
and the dependent variable for all non‑null data pairs of the dependent and
independent variable arguments. The function considers all the valid columns
in teradataml DataFrame as dependent variable and "expression" as an independent
variable.
Note:
When there are fewer than two non-null data point pairs in the
data used for the computation, the function returns None.
PARAMETERS:
expression:
Required Argument.
Specifies a ColumnExpression of a column or name of the column or a
literal representing an independent variable for the regression.
An independent variable is something that is varied under your control
to test the behavior of another variable.
Types: ColumnExpression OR int OR float OR str
RETURNS:
teradataml DataFrame
RAISES:
RuntimeError - If none of the columns support the aggregate operation.
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", "admissions_train")
>>>
# Create a DataFrame on 'admissions_train' table.
>>> admissions_train = DataFrame("admissions_train")
>>> admissions_train
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
19 yes 1.98 Advanced Advanced 0
>>>
# Example 1: Calculate the sum of the products of the column 'gpa'
# for all non-null data pairs with dependent variable as all other
# valid columns.
>>> df = admissions_train.regr_sxy(admissions_train.gpa)
>>> df
regr_sxy_id regr_sxy_gpa regr_sxy_admitted
0 -3.255 10.294177 -0.2155
>>>
# Example 2: Calculate the sum of the products of the column 'gpa'
# for all non-null data pairs with dependent variable as all other
# valid columns, for each level of 'programming'.
>>> df = admissions_train.groupby("programming").regr_sxy(admissions_train.gpa)
>>> df
programming regr_sxy_id regr_sxy_gpa regr_sxy_admitted
0 Advanced 8.696875 3.660394 1.456875
1 Novice 1.871818 4.182673 -0.346364
2 Beginner -16.990000 1.509800 -0.900000
>>>