featuretools.primitives.CountBelowMean#

class featuretools.primitives.CountBelowMean(skipna=True)[source]#

Determines the number of values that are below the mean.

Parameters:

skipna (bool) – Determines if to use NA/null values. Defaults to True to skip NA/null.

Examples

>>> count_below_mean = CountBelowMean()
>>> count_below_mean([1, 2, 3, 4, 10])
3

The way NaNs are treated can be controlled.

>>> count_below_mean_skipna = CountBelowMean(skipna=False)
>>> count_below_mean_skipna([1, 2, 3, 4, 5, None])
nan
__init__(skipna=True)[source]#

Methods

__init__([skipna])

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()

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