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- stddev_pop(value_expression)
- DESCRIPTION:
Function returns the population standard deviation for the non-null data
points in value_expression.
The standard deviation is the second moment of a population. For a population,
it is a measure of dispersion from the mean of that population.
PARAMETERS:
value_expression:
Required Argument.
Specifies a ColumnExpression of a numeric column for which population
standard deviation is to be computed.
Format for the argument: '<dataframe>.<dataframe_column>.expression'.
Notes:
1. Nulls are not included in the result computation.
2. When there are no non-null data points in the population, the
function returns NULL.
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 1: Calculate the population standard deviation for the values in
# "gpa" column.
# Import func from sqlalchemy to execute stddev_pop function.
>>> from sqlalchemy import func
# Create a sqlalchemy Function object.
>>> stddev_pop_func_ = func.stddev_pop(admissions_train.gpa.expression)
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(True, stddev_pop_gpa_=stddev_pop_func_)
>>> print(df)
stddev_pop_gpa_
0 0.507301
>>>
# Example 2: Calculate the population standard deviation for the values in
# "gpa" column for each level of programming.
# Note:
# When assign() is run after DataFrame.groupby(), the function ignores
# the "drop_columns" argument.
>>> admissions_train.groupby("programming").assign(stddev_pop_gpa_=func.stddev_pop(admissions_train.gpa.expression))
programming stddev_pop_gpa_
0 Advanced 0.478304
1 Novice 0.616638
2 Beginner 0.340791
>>>
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