Teradata Package for Python Function Reference on VantageCloud Lake - regr_avgx - 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.dataframe.DataFrame.regr_avgx = regr_avgx(expression)
- DESCRIPTION:
Function returns the column-wise mean of the independent variable for all
non-null data pairs of the dependent and an 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 mean of the column 'gpa' for all non-null
# data pairs with dependent variable as all other valid columns.
>>> df = admissions_train.regr_avgx(admissions_train.gpa)
>>> df
regr_avgx_id regr_avgx_gpa regr_avgx_admitted
0 3.54175 3.54175 3.54175
>>>
# Example 2: Calculate the mean of the column 'gpa' for all non-null
# data pairs with independent variable as all other valid columns,
# for each level of 'programming'.
>>> df = admissions_train.groupby("programming").regr_avgx(admissions_train.gpa)
>>> df
programming regr_avgx_id regr_avgx_gpa regr_avgx_admitted
0 Advanced 3.615625 3.615625 3.615625
1 Novice 3.294545 3.294545 3.294545
2 Beginner 3.660000 3.660000 3.660000
>>>