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- case_n(conditional_column_expressions)
- DESCRIPTION:
Function evaluates a list of conditions and returns the position of the first
condition that evaluates to TRUE, provided that no prior condition in the
list evaluates to UNKNOWN.
PARAMETERS:
conditional_column_expressions:
Required Argument.
Specifies a condition column expression or multiple comma-separated condition
column expressions to evaluate.
Format for the column_expression: '<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 case_n() function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object.
# Note: We pass multiple conditional column expressions separated by comma.
>>> case_n_func_ = func.case_N(admissions_train.stats.expression == 'Novice',
... admissions_train.stats.expression == 'Beginner')
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(case_n_func_=case_n_func_)
>>> print(df)
masters gpa stats programming admitted case_n_func_
id
15 yes 4.00 Advanced Advanced 1 NaN
7 yes 2.33 Novice Novice 1 1.0
22 yes 3.46 Novice Beginner 0 1.0
17 no 3.83 Advanced Advanced 1 NaN
13 no 4.00 Advanced Novice 1 NaN
38 yes 2.65 Advanced Beginner 1 NaN
26 yes 3.57 Advanced Advanced 1 NaN
5 no 3.44 Novice Novice 0 1.0
34 yes 3.85 Advanced Beginner 0 NaN
40 yes 3.95 Novice Beginner 0 1.0
>>>
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