featuretools.primitives.CountBelowMean#

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

确定低于均值的数值数量.

Parameters:

skipna (bool) – 确定是否使用NA/null值.默认为True,跳过NA/null.

Examples

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

可以控制如何处理NaNs.

>>> 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)

将嵌套的列模式输入展平成一个列表.

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