featuretools.primitives.Entropy¶

class featuretools.primitives.Entropy(dropna=False, base=None)[source]¶

Calculates the entropy for a categorical column

Description:: Given a list of observations from a categorical column return the entropy of the distribution. NaN values can be treated as a category or dropped.

Parameters

dropna (bool) – Whether to consider NaN values as a separate category Defaults to False.
base (float) – The logarithmic base to use Defaults to e (natural logarithm)

Examples

>>> pd_entropy = Entropy()
>>> pd_entropy([1,2,3,4])
1.3862943611198906

Methods

`__init__`([dropna, base])
`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.Trend

featuretools.primitives.IsIn