class_docstr: Сlass for storing, cleaning and processing your dataset
class_docstr_tok: ["\u0421lass","for","storing",",","cleaning","and","processing","your","dataset"]
docstr: Description of group_encoder
  
  Args:
      data (pd.DataFrame): dataset
      cat_columns (list): cat columns names
      num_column (str): num column name
  
  Returns:
      pd.DataFrame
docstr_tok: ["Description","of","group_encoder","Args",":","data","(","pd.DataFrame",")",":","dataset","cat_columns","(","list",")",":","cat","columns","names","num_column","(","str",")",":","num","column","name","Returns",":","pd.DataFrame"]
code: def gen_groupby_cat_encode_features(
      self,
      data,
      cat_columns,
      num_column,
      cat_encoder_name="JamesSteinEncoder",
  ):
      """
      Description of group_encoder
  
      Args:
          data (pd.DataFrame): dataset
          cat_columns (list): cat columns names
          num_column (str): num column name
  
      Returns:
          pd.DataFrame
  
      """
  
      if isinstance(cat_encoder_name, str):
          if cat_encoder_name in self.cat_encoder_names_list:
              encoder = JamesSteinEncoder(
                  cols=self.cat_features,
                  model="beta",
                  return_df=True,
                  drop_invariant=True,
              )
              encoder.fit(
                  X=data[cat_columns], y=data[num_column].values
              )
          else:
              print(f"{cat_encoder_name} is not supported!")
              return ("", "")
      else:
          encoder = copy.deepcopy(cat_encoder_name)
  
      data_encodet = encoder.transform(
          X=data[cat_columns], y=data[num_column].values
      )
      data_encodet = data_encodet.add_prefix(
          "GroupEncoded_" + num_column + "_"
      )
  
      return (data_encodet, encoder)
  
imports: import pandas
  import numpy
  from itertools import combinations
  from sklearn.preprocessing import StandardScaler
  from category_encoders import HashingEncoder, SumEncoder, PolynomialEncoder, BackwardDifferenceEncoder
  from category_encoders import OneHotEncoder, HelmertEncoder, OrdinalEncoder, CountEncoder, BaseNEncoder
  from category_encoders import TargetEncoder, CatBoostEncoder, WOEEncoder, JamesSteinEncoder
  from category_encoders.glmm import GLMMEncoder
  from sklearn.preprocessing import LabelEncoder
  from category_encoders.wrapper import PolynomialWrapper
  from .encoders import FrequencyEncoder
  from .None import settings
  import pdb
  import copy
  import dask
  import dask.dataframe
label_desc: Use image node_id 8 for calling the DataBunch obj's underlying member method code with example usage: obj.gen_groupby_cat_encode_features(data, cat_columns, num_column, cat_encoder_name) and returns: data_encodet,  encoder, str,  str