class_docstr: Label Encoder that groups infrequent values into one label.
  
  Code from https://github.com/jeongyoonlee/Kaggler/blob/master/kaggler/preprocessing/data.py
  
  Attributes:
      min_obs (int): minimum number of observation to assign a label.
      label_encoders (list of dict): label encoders for columns
      label_maxes (list of int): maximum of labels for columns
docstr: Encode one categorical column into labels.
  
  Args:
      x (pandas.Series): a categorical column to encode
      i (int): column index
  
  Returns:
      x (pandas.Series): a column with labels.
docstr_tok: ["Encode","one","categorical","column","into","labels",".","Args",":","x","(","pandas.Series",")",":","a","categorical","column","to","encode","i","(","int",")",":","column","index","Returns",":","x","(","pandas.Series",")",":","a","column","with","labels","."]
code: def _transform_col(self, x, i):
      """Encode one categorical column into labels.
  
      Args:
          x (pandas.Series): a categorical column to encode
          i (int): column index
  
      Returns:
          x (pandas.Series): a column with labels.
      """
      return x.fillna(NAN_INT).map(self.label_encoders[i]).fillna(0)
  
imports: import logging
  import numpy
  import pandas
  from scipy import sparse
  from sklearn import base
label_desc: Use image node_id 4 for calling the LabelEncoder obj's underlying member method code with example usage: obj._transform_col(x, i) and returns: x