featuretools.primitives.Skew¶
- class featuretools.primitives.Skew[source]¶
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__
()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
([agg_type])Attributes
base_of
base_of_exclude
commutative
compatibility
Additional compatible libraries
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