class_docstr: One-Hot-Encoder that groups infrequent values into one dummy variable.
  
  Code from https://github.com/jeongyoonlee/Kaggler/blob/master/kaggler/preprocessing/data.py
  
  Attributes:
      min_obs (int): minimum number of observation to create a dummy variable
      label_encoders (list of (dict, int)): label encoders and their maximums
                                            for columns
docstr: Initialize the OneHotEncoder class object.
  
  Args:
      min_obs (int): minimum number of observation to create a dummy variable
docstr_tok: ["Initialize","the","OneHotEncoder","class","object",".","Args",":","min_obs","(","int",")",":","minimum","number","of","observation","to","create","a","dummy","variable"]
code: def __init__(self, min_obs=10):
      """Initialize the OneHotEncoder class object.
  
      Args:
          min_obs (int): minimum number of observation to create a dummy variable
      """
  
      self.min_obs = min_obs
      self.label_encoder = LabelEncoder(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 OneHotEncoder object from inherited base classes: base with example: obj = OneHotEncoder(min_obs)