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: Initialize the LabelEncoder class object.
  
  Args:
      min_obs (int): minimum number of observation to assign a label.
docstr_tok: ["Initialize","the","LabelEncoder","class","object",".","Args",":","min_obs","(","int",")",":","minimum","number","of","observation","to","assign","a","label","."]
code: def __init__(self, min_obs=10):
      """Initialize the LabelEncoder class object.
  
      Args:
          min_obs (int): minimum number of observation to assign a label.
      """
  
      self.min_obs = min_obs
  
imports: import logging
  import numpy
  import pandas
  from scipy import sparse
  from sklearn import base
label_desc: Use image node_id 1 to create a new LabelEncoder object from inherited base classes: base with example: obj = LabelEncoder(min_obs)