featuretools.primitives.CountBelowMean

class featuretools.primitives.CountBelowMean(skipna=True)

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)

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
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