featuretools.primitives.NMostCommonFrequency

class featuretools.primitives.NMostCommonFrequency(n=3, skipna=True)

Determines the frequency of the n most common items.

Parameters:

• n (int) – defines “n” in “n most common”. Defaults to 3.

• skipna (bool) – Determines if to use NA/null values. Defaults to True to skip NA/null.

Description:

Given a list, find the n most common items, and return a series showing the frequency of each item. If the list has less than n unique values, the resulting series will be padded with nan.

Examples

>>> n_most_common_frequency = NMostCommonFrequency()
>>> n_most_common_frequency([1, 1, 1, 2, 2, 3, 4, 4]).to_list()
[3, 2, 2]

We can increase n to include more items.

>>> n_most_common_frequency = NMostCommonFrequency(4)
>>> n_most_common_frequency([1, 1, 1, 2, 2, 3, 4, 4]).to_list()
[3, 2, 2, 1]

NaNs are skipped by default.

>>> n_most_common_frequency = NMostCommonFrequency(3)
>>> n_most_common_frequency([1, 1, 1, 2, 2, 3, 4, 4, None, None, None]).to_list()
[3, 2, 2]

However, the way NaNs are treated can be controlled.

>>> n_most_common_frequency = NMostCommonFrequency(3, skipna=False)
>>> n_most_common_frequency([1, 1, 1, 2, 2, 3, 4, 4, None, None, None]).to_list()
[3, 3, 2]

__init__(n=3, skipna=True)

Methods

__init__([n, skipna])
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