featuretools.primitives.DivideNumericScalar#

class featuretools.primitives.DivideNumericScalar(value=1)[source]#

将列表中的每个元素除以一个标量.

Examples

>>> divide_numeric_scalar = DivideNumericScalar(value=2)
>>> divide_numeric_scalar([3, 1, 2]).tolist()
[1.5, 0.5, 1.0]

Methods

`__init__`([value])
`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`
`uses_full_dataframe`