Teradata Package for Python Function Reference on VantageCloud Lake - covar_pop - 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.covar_pop = covar_pop(expression)
- DESCRIPTION:
Function returns the column-wise population covariance of its arguments
for all non-null data point pairs. Covariance measures whether or not
two random variables vary in the same way. It is the average of the
products of deviations for each non-null data point pair.
Notes:
1. When there are no non-null data point pairs in the data used for
the computation, the function returns None.
2. High covariance does not imply a causal relationship between
the variables.
PARAMETERS:
expression:
Required Argument.
Specifies a ColumnExpression of a numeric column or name of the column
or a numeric literal to be paired with another variable to determine
their population covariance.
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 population covariance between 'admitted' and all the
# valid columns in teradataml DataFrame.
>>> df = admissions_train.covar_pop(admissions_train.admitted)
>>> df
covar_pop_id covar_pop_gpa covar_pop_admitted
0 -1.075 -0.005387 0.2275
>>>
# Example 2: Calculate population covariance between 'admitted' and all the
# valid columns in teradataml DataFrame, for each level of 'programming'.
>>> df = admissions_train.groupby("programming").covar_pop(admissions_train.admitted)
>>> df
programming covar_pop_id covar_pop_gpa covar_pop_admitted
0 Beginner 0.171598 -0.069231 0.236686
1 Advanced -0.597656 0.091055 0.152344
2 Novice -0.900826 -0.031488 0.198347
>>>