NOTICE

The upcoming release of Featuretools 1.0.0 contains several breaking changes. Users are encouraged to test this version prior to release by installing from GitHub:

pip install https://github.com/alteryx/featuretools/archive/woodwork-integration.zip

For details on migrating to the new version, refer to Transitioning to Featuretools Version 1.0. Please report any issues in the Featuretools GitHub repo or by messaging in Alteryx Open Source Slack.

featuretools.primitives.Entropy

class featuretools.primitives.Entropy(dropna=False, base=None)

Calculates the entropy for a categorical variable

Description:

Given a list of observations from a categorical variable 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)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([dropna, base])	Initialize self.
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
default_value
description_template
input_types
max_stack_depth
name
number_output_features
stack_on
stack_on_exclude
stack_on_self
uses_calc_time

featuretools.primitives.Trend featuretools.primitives.IsIn