Teradata Package for Python Function Reference on VantageCloud Lake - regr_sxx - 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.regr_sxx = regr_sxx(expression)
- DESCRIPTION:
Function returns the sum of the squares of the independent variable
expression for all non-null data pairs of dependent and an independent
variable arguments. When function is executed, "expression" is treated as
an independent variable and dependent variable is ColumnExpression
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:
ColumnExpression, also known as, teradataml DataFrameColumn.
RAISES:
RuntimeError - If column does not support the aggregate operation.
NOTES:
* One must use DataFrame.assign() when using the aggregate functions on
ColumnExpression, also known as, teradataml DataFrameColumn.
* One should always use "drop_columns=True" in DataFrame.assign(), while
running the aggregate operation on teradataml DataFrame.
* "drop_columns" argument in DataFrame.assign() is ignored, when aggregate
function is operated on DataFrame.groupby().
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 squares of the values in "gpa" column
# (independent variable) with respect to values in "admitted"
# column (dependent variable).
# Execute regr_sxx() using teradataml DataFrameColumn to generate the ColumnExpression.
>>> regr_sxx_column = admissions_train.admitted.regr_sxx(admissions_train.gpa)
# Pass the generated ColumnExpression to DataFrame.assign(), to run and produce the result.
>>> df = admissions_train.assign(True, regr_sxx_=regr_sxx_column)
>>> df
regr_sxx_
0 10.294177
>>>
# Example 2: Calculate the sum of the squares of the values in "gpa" column
# (independent variable) with respect to values in "admitted"
# column (dependent variable) for each level of programming.
# Note:
# When assign() is run after DataFrame.groupby(), the function ignores
# the "drop_columns" argument.
# Execute regr_sxx() using teradataml DataFrameColumn to generate the ColumnExpression.
>>> regr_sxx_column = admissions_train.admitted.regr_sxx(admissions_train.gpa)
# Pass the generated ColumnExpression to DataFrame.assign(), to run and produce the result.
>>> df = admissions_train.groupby("programming").assign(regr_sxx_=regr_sxx_column)
>>> df
programming regr_sxx_
0 Advanced 3.660394
1 Novice 4.182673
2 Beginner 1.509800
>>>