# 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]
```
__init__(method='ffill', limit=None)[source]#

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` `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`