featuretools.primitives.AbsoluteDiff#
- class featuretools.primitives.AbsoluteDiff(method='ffill', limit=None)[source]#
- Calculates the absolute difference from the previous element
in a list of numbers.
- Description:
The absolute difference from the previous element is computed for all elements in the input. The first item in the output will always be nan, since there is no previous element for the first element. Elements in the input containing nan will be filled using either a forward-fill or backward-fill method, specified by the method argument.
- Parameters:
method (str) –
Method to use for filling nan values in reindexed Series. Possible values are [‘pad’, ‘ffill’, ‘backfill’, ‘bfill’]. Default is ‘ffill’.
- pad / ffill: propagate last valid observation forward
to fill gap
- backfill / bfill: propagate next valid observation backward
to fill gap
limit (int) – The max number of consecutive NaN values in a gap that can be filled. Default is None.
Examples
>>> absolute_diff = AbsoluteDiff() >>> absolute_diff([2, 5, 15, 3]).tolist() [nan, 3.0, 10.0, 12.0]
Forward filling of input elements using the ‘ffill’ argument
>>> absolute_diff_ffill = AbsoluteDiff(method="ffill") >>> absolute_diff_ffill([None, 5, 10, 20, None, 10, None]).tolist() [nan, nan, 5.0, 10.0, 0.0, 10.0, 0.0]
Backward filling of input element using the ‘bfill’ argument
>>> absolute_diff_bfill = AbsoluteDiff(method="bfill") >>> absolute_diff_bfill([None, 5, 10, 20, None, 10, None]).tolist() [nan, 0.0, 5.0, 10.0, 10.0, 0.0, nan]
The number of nan values that are filled can be limited
>>> absolute_diff_limitfill = AbsoluteDiff(limit=2) >>> absolute_diff_limitfill([2, None, None, None, 3, 1]).tolist() [nan, 0.0, 0.0, nan, nan, 2.0]
Methods
__init__([method, limit])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_ofbase_of_excludecommutativecompatibilityAdditional compatible libraries
default_valueDefault value this feature returns if no data found.
description_templateinput_typeswoodwork.ColumnSchema types of inputs
max_stack_depthnameName of the primitive
number_output_featuresNumber of columns in feature matrix associated with this feature
return_typeColumnSchema type of return
series_librarystack_onstack_on_excludestack_on_selfuses_calc_timeuses_full_dataframe