Teradata Package for Python Function Reference | 20.00 - regr_intercept - 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_intercept = regr_intercept(expression)
- DESCRIPTION:
Function returns the column-wise intercept of the univariate linear regression line
through all non-null data pairs of the dependent and independent variable
arguments. The intercept is the point at which the regression line through
the non-null data pairs in the sample intersects the ordinate, or y-axis,
of the graph. The plot of the linear regression on the variables is used to
predict the behavior of the dependent variable from the change in the
independent variable. There can be a strong nonlinear relationship between
independent and dependent variables, and the computation of the simple linear
regression between such variable pairs does not reflect such a relationship.
The function considers all the valid columns in teradataml DataFrame as
dependent variable and "expression" as an independent variable.
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 intercept of the univariate linear regression
# line through all non-null data pairs for all the applicable
# columns and independent variable as 'gpa'.
>>> df = admissions_train.regr_intercept(admissions_train.gpa)
>>> df
regr_intercept_id regr_intercept_gpa regr_intercept_admitted
0 21.619895 0.0 0.724144
>>>
# Example 2: Calculate the intercept of the univariate linear regression
# line through all non-null data pairs for all the applicable
# columns and independent variable as 'gpa', for each level
# of 'programming'
>>> df = admissions_train.groupby("programming").regr_intercept(admissions_train.gpa)
>>> df
programming regr_intercept_id regr_intercept_gpa regr_intercept_admitted
0 Advanced 8.221993 0.0 -0.626557
1 Novice 18.889270 0.0 1.000091
2 Beginner 66.340361 0.0 2.566361
>>>