featuretools.primitives.Skew

class featuretools.primitives.Skew

Computes the extent to which a distribution differs from a normal distribution.

Description:

For normally distributed data, the skewness should be about 0. A skewness value > 0 means that there is more weight in the left tail of the distribution.

Examples

>>> skew = Skew()
>>> skew([1, 10, 30, None])
1.0437603722639681

__init__()

Methods

__init__()
flatten_nested_input_types(input_types)	Flattens nested column schema inputs into a single list.
generate_name(base_feature_names, ...)
generate_names(base_feature_names, ...)
get_args_string()
get_arguments()
get_description(input_column_descriptions[, ...])
get_filepath(filename)
get_function()

Attributes

base_of
base_of_exclude
commutative
default_value	Default value this feature returns if no data found.
description_template
input_types	woodwork.ColumnSchema types of inputs
max_stack_depth
name	Name of the primitive
number_output_features	Number of columns in feature matrix associated with this feature
return_type	ColumnSchema type of return
stack_on
stack_on_exclude
stack_on_self
uses_calc_time