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_of
base_of_exclude
commutative
compatibility
Additional compatible libraries
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
series_library
stack_on
stack_on_exclude
stack_on_self
uses_calc_time
uses_full_dataframe