class_docstr: Encodes missing values for both numerical and categorical features.
  
  Several strategies are possible in each case.
  
  Parameters
  ----------
  numerical_strategy : str or float or int. default = "mean"
      The strategy to encode NA for numerical features.
      Available strategies = "mean", "median",
      "most_frequent" or a float/int value
  
  categorical_strategy : str, default = '<NULL>'
      The strategy to encode NA for categorical features.
      Available strategies = a string or "most_frequent"
docstr: Init a NA_encoder.
  
  User can choose numerical strategy and categorical strategy.
  
  Parameters
  ----------
  numerical_strategy : str or float or int. default = "mean"
      The strategy to encode NA for numerical features.
  
  categorical_strategy : str, default = '<NULL>'
      The strategy to encode NA for categorical features.
code: def __init__(
      self, numerical_strategy="mean", categorical_strategy="<NULL>"
  ):
      """Init a NA_encoder.
  
      User can choose numerical strategy and categorical strategy.
  
      Parameters
      ----------
      numerical_strategy : str or float or int. default = "mean"
          The strategy to encode NA for numerical features.
  
      categorical_strategy : str, default = '<NULL>'
          The strategy to encode NA for categorical features.
  
      """
      self.numerical_strategy = numerical_strategy
      self.categorical_strategy = categorical_strategy
      self.__Lcat = []
      self.__Lnum = []
      self.__imp = None
      self.__mode = dict()
      self.__fitOK = False
  
file_path: MLBox/mlbox/encoding/na_encoder.py
imports: import pandas
  import warnings
  from sklearn.impute import SimpleImputer
label_desc: Use image node_id 1 to create a new NA_encoder object  with example: obj = NA_encoder(numerical_strategy, categorical_strategy)