class_docstr: Determines the frequency of the n most common items.
  
  Args:
      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]
  
      `NaN`s 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 `NaN`s 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]
code: def __init__(self, n=3, skipna=True):
      self.n = n
      self.number_output_features = n
      self.skipna = skipna
  
file_path: featuretools/featuretools/primitives/standard/aggregation/n_most_common_frequency.py
imports: import numpy
  import pandas
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Categorical
  from featuretools.primitives.base import AggregationPrimitive
label_desc: Use image node_id 1 to create a new NMostCommonFrequency object from inherited base classes: AggregationPrimitive with example: obj = NMostCommonFrequency(n, skipna)