featuretools.primitives.NumConsecutiveLessMean#

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

确定低于均值的最长子序列的长度.

Description:

给定一个数字列表,找到数值小于整个序列均值的最长子序列.返回该最长子序列的长度.

Parameters:

skipna (bool) – 如果为False且x中存在任何`NaN`值,则结果将为`NaN`.如果为True,则跳过`NaN`值.默认为True.

Examples

>>> num_consecutive_less_mean = NumConsecutiveLessMean()
>>> num_consecutive_less_mean([1, 2, 3, 4, 5, 6])
3.0

我们还可以控制`NaN`值的处理方式.

>>> num_consecutive_less_mean = NumConsecutiveLessMean(skipna=False)
>>> num_consecutive_less_mean([1, 2, 3, 4, 5, 6, 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