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- ln(column_expression_or_constant)
- DESCRIPTION:
Function returns the natural logarithm of the argument.
PARAMETERS:
column_expression_or_constant:
Required Argument.
Specifies a ColumnExpression of a column containing numeric values or a numeric constant
to compute the natural logarithm.
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 computes natural logarithm of values in "gpa" and a constant 10.
# Import func from sqlalchemy to execute ln() function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object and pass the Function object as input to DataFrame.assign().
# Note: We are using 'ln' and 'LN' as function names. Function names are case-insensitive.
>>> df = admissions_train.assign(natural_log_gpa_func_ = func.ln(admissions_train.gpa.expression),
... natural_log_const_func_ = func.LN(10))
>>> print(df)
masters gpa stats programming admitted natural_log_const_func_ natural_log_gpa_func_
id
22 yes 3.46 Novice Beginner 0 2.302585 1.241269
36 no 3.00 Advanced Novice 0 2.302585 1.098612
15 yes 4.00 Advanced Advanced 1 2.302585 1.386294
38 yes 2.65 Advanced Beginner 1 2.302585 0.974560
5 no 3.44 Novice Novice 0 2.302585 1.235471
17 no 3.83 Advanced Advanced 1 2.302585 1.342865
34 yes 3.85 Advanced Beginner 0 2.302585 1.348073
13 no 4.00 Advanced Novice 1 2.302585 1.386294
26 yes 3.57 Advanced Advanced 1 2.302585 1.272566
19 yes 1.98 Advanced Advanced 0 2.302585 0.683097
>>>
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