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

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`([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`

featuretools.primitives.Last

featuretools.primitives.Trend