[docs]classAbsoluteDiff(TransformPrimitive):"""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. Args: 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] """name="absolute_diff"input_types=[ColumnSchema(semantic_tags={"numeric"})]return_type=ColumnSchema(semantic_tags={"numeric"})