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- mod(column_expression_or_constant1, column_expression_or_constant2)
- DESCRIPTION:
Function returns the remainder (modulus) of dividend (column_expression_or_constant1)
divided by divisor (column_expression_or_constant1).
PARAMETERS:
column_expression_or_constant1:
Required Argument.
Specifies a ColumnExpression of a numeric column or a constant value that is the dividend.
Format for the argument: '<dataframe>.<dataframe_column>.expression'.
column_expression_or_constant2:
Required Argument.
Specifies a ColumnExpression of a numeric column or a constant value that is the divisor.
Format for the argument: '<dataframe>.<dataframe_column>.expression'.
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
>>>
# Import func from sqlalchemy to execute mod() function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object and pass the Function object as input to DataFrame.assign().
# Note: We are using 'mod' and 'MOD' as function names. Function names are case-insensitive.
>>> df = admissions_train.assign(modgpa2 = func.mod(admissions_train.gpa.expression, 2),
... modgpa12 = func.MOD(12, admissions_train.gpa.expression))
>>> print(df)
masters gpa stats programming admitted modgpa12 modgpa2
id
22 yes 3.46 Novice Beginner 0 1.62 1.46
36 no 3.00 Advanced Novice 0 0.00 1.00
15 yes 4.00 Advanced Advanced 1 0.00 0.00
38 yes 2.65 Advanced Beginner 1 1.40 0.65
5 no 3.44 Novice Novice 0 1.68 1.44
17 no 3.83 Advanced Advanced 1 0.51 1.83
34 yes 3.85 Advanced Beginner 0 0.45 1.85
13 no 4.00 Advanced Novice 1 0.00 0.00
26 yes 3.57 Advanced Advanced 1 1.29 1.57
19 yes 1.98 Advanced Advanced 0 0.12 1.98
>>>
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