Teradata Package for Python Function Reference on VantageCloud Lake - regr_count - 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_count = regr_count(expression)
- DESCRIPTION:
Function returns the column-wise count of 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 count of the column 'gpa' for all non-null
# data pairs with dependent variable as all other columns.
>>> df = admissions_train.regr_count(admissions_train.gpa)
>>> df
regr_count_id regr_count_gpa regr_count_admitted
0 40 40 40
>>>
# Example 2: Calculate the count of the column 'gpa' for all non-null
# data pairs with dependent variable as all other columns,
# for each level of 'programming'.
>>> df = admissions_train.groupby("programming").regr_count(admissions_train.gpa)
>>> df
programming regr_count_id regr_count_gpa regr_count_admitted
0 Advanced 16 16 16
1 Novice 11 11 11
2 Beginner 13 13 13
>>>