featuretools.primitives.CityblockDistance

class featuretools.primitives.CityblockDistance(unit='miles')

Calculates the distance between points in a city road grid.

Description:

This distance is calculated using the haversine formula, which takes into account the curvature of the Earth. If either input data contains NaN`s, the calculated distance with be `NaN. This calculation is also known as the Mahnattan distance.

Parameters:

unit (str) – Determines the unit value to output. Could be miles or kilometers. Default is miles.

Examples

>>> cityblock_distance = CityblockDistance()
>>> DC = (38, -77)
>>> Boston = (43, -71)
>>> NYC = (40, -74)
>>> distances_mi = cityblock_distance([DC, DC], [NYC, Boston])
>>> np.round(distances_mi, 3).tolist()
[301.519, 672.089]

We can also change the units in which the distance is calculated.

>>> cityblock_distance_kilometers = CityblockDistance(unit='kilometers')
>>> distances_km = cityblock_distance_kilometers([DC, DC], [NYC, Boston])
>>> np.round(distances_km, 3).tolist()
[485.248, 1081.622]

__init__(unit='miles')

Methods

__init__([unit])
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