Teradata Package for Python Function Reference | 20.00 - hypot - 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
Published
March 2024
Language
English (United States)
Last Update
2024-04-10
dita:id
TeradataPython_FxRef_Enterprise_2000
Product Category
Teradata Vantage
teradataml.dataframe.sql.DataFrameColumn.hypot = hypot(self, other)
DESCRIPTION:
    Function to compute the hypotenuse.
 
PARAMETERS:
    other:
        Required Argument.
        Specifies DataFrame column for calculation of hypotenuse.
        Types: int or float or str or ColumnExpression
 
Returns:
    ColumnExpression
 
Examples:
    # Load the data to run the example.
    >>> load_example_data("teradataml","titanic")
 
    # Create a DataFrame on 'titanic' table.
    >>> titanic = DataFrame.from_table('titanic')
    >>> df = titanic.select(["passenger", "age", "fare"])
    >>> print(df)
                age      fare
    passenger
    326        36.0  135.6333
    183         9.0   31.3875
    652        18.0   23.0000
    265         NaN    7.7500
    530        23.0   11.5000
    122         NaN    8.0500
    591        35.0    7.1250
    387         1.0   46.9000
    734        23.0   13.0000
    795        25.0    7.8958
    >>>
 
    # Example 1: compute hypotenuse of two columns fare and age.
    >>> hypot_df = df.assign(hypot_column=df.fare.hypot(titanic.age))
    >>> print(hypot_df)
                age      fare  hypot_column
    passenger
    326        36.0  135.6333    140.329584
    183         9.0   31.3875     32.652338
    652        18.0   23.0000     29.206164
    40         14.0   11.2417     17.954827
    774         NaN    7.2250           NaN
    366        30.0    7.2500     30.863611
    509        28.0   22.5250     35.935715
    795        25.0    7.8958     26.217240
    61         22.0    7.2292     23.157317
    469         NaN    7.7250           NaN
    >>>