Teradata Package for Python Function Reference on VantageCloud Lake - cosh - 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.cosh = cosh(column_expression)
- DESCRIPTION:
Function computes the hyperbolic cosine value of the values in column.
NOTES:
1. If the type of the column is not FLOAT, column values are converted to FLOAT
based on implicit type conversion rules. If the value cannot be converted, an
error is reported.
2. Unsupported column types:
a. BYTE or VARBYTE
b. LOBs (BLOB or CLOB)
c. CHARACTER or VARCHAR if the server character set is GRAPHIC
RAISES:
TypeError, ValueError, TeradataMlException
RETURNS:
DataFrameColumn
EXAMPLES:
# Load the data to execute the example.
>>> load_example_data("dataframe", "admissions_train")
# Create a DataFrame on 'admissions_train' table.
>>> df = DataFrame("admissions_train").iloc[:4]
>>> print(df)
masters gpa stats programming admitted
id
3 no 3.70 Novice Beginner 1
4 yes 3.50 Beginner Novice 1
2 yes 3.76 Beginner Beginner 0
1 yes 3.95 Beginner Beginner 0
# Example 1: Calculates hyperbolic cos value for the "gpa" column and pass it as
# input to DataFrame.assign().
>>> result = df.assign(col=df.gpa.cosh())
>>> print(result)
masters gpa stats programming admitted col
id
3 no 3.70 Novice Beginner 1 20.236014
4 yes 3.50 Beginner Novice 1 16.572825
2 yes 3.76 Beginner Beginner 0 21.485855
1 yes 3.95 Beginner Beginner 0 25.977311
# Example 2: Filter the rows where hyperbolic cosine of values in "gpa" column
# are greater than 21.
>>> print(df[df.gpa.cosh() > 21])
masters gpa stats programming admitted
id
2 yes 3.76 Beginner Beginner 0
1 yes 3.95 Beginner Beginner 0