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
__init__(dropna=False, base=None)[source]#

Methods

__init__([dropna, base])

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

series_library

stack_on

stack_on_exclude

stack_on_self

uses_calc_time