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- radians(column_expression_or_constant)
- DESCRIPTION:
Function takes a value specified in degrees and converts it to radians.
PARAMETERS:
column_expression_or_constant:
Required Argument.
Specifies a ColumnExpression containing degrees values or constant representing a degrees value.
Format for the argument: '<dataframe>.<dataframe_column>.expression'.
Notes:
1. If the type of the column/argument is not FLOAT, column values are converted to FLOAT
based on implicit type conversion rules. If an argument cannot be converted, an
error is reported. For more information on implicit type conversion,
see Teradata Vantage™ Data Types and Literals, B035-1143.
2. Unsupported column types:
a. BYTE or VARBYTE
b. LOBs (BLOB or CLOB)
c. CHARACTER or VARCHAR if the server character set is GRAPHIC
NOTE:
Function accepts positional arguments only.
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 converts values in the "admitted" column to radians with help of SQLAlchemy.
# Import func from sqlalchemy to execute radians() function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object.
>>> radians_func_ = func.radians(admissions_train.admitted.expression)
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(radians_admitted_func_ = radians_func_)
>>> print(df)
masters gpa stats programming admitted radians_admitted_func_
id
22 yes 3.46 Novice Beginner 0 0.000000
36 no 3.00 Advanced Novice 0 0.000000
15 yes 4.00 Advanced Advanced 1 0.017453
38 yes 2.65 Advanced Beginner 1 0.017453
5 no 3.44 Novice Novice 0 0.000000
17 no 3.83 Advanced Advanced 1 0.017453
34 yes 3.85 Advanced Beginner 0 0.000000
13 no 4.00 Advanced Novice 1 0.017453
26 yes 3.57 Advanced Advanced 1 0.017453
19 yes 1.98 Advanced Advanced 0 0.000000
>>>
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