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- atanh(column_expression)
- DESCRIPTION:
Function computes the inverse hyperbolic tangent value of an argument.
PARAMETERS:
column_expression:
Required Argument.
Specifies a ColumnExpression of a numeric column or a numeric constant
on which atanh() is requested.
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.
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 calculates inverse hyperbolic tan value for the "gpa column values/10" with help of SQLAlchemy.
# Import func from sqlalchemy to execute atanh() function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object.
>>> atanh_func_ = func.atanh(admissions_train.gpa.expression / 10)
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(atanh_gpa_=atanh_func_)
>>> print(df)
masters gpa stats programming admitted atanh_gpa_
id
15 yes 4.00 Advanced Advanced 1 0.423649
7 yes 2.33 Novice Novice 1 0.237359
22 yes 3.46 Novice Beginner 0 0.360893
17 no 3.83 Advanced Advanced 1 0.403571
13 no 4.00 Advanced Novice 1 0.423649
38 yes 2.65 Advanced Beginner 1 0.271478
26 yes 3.57 Advanced Advanced 1 0.373443
5 no 3.44 Novice Novice 0 0.358622
34 yes 3.85 Advanced Beginner 0 0.405917
40 yes 3.95 Novice Beginner 0 0.417711
>>>
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