code: def _encoding_for_parallel(data: DataFrame, column_name_index_map):
      # each condition can be indexed from those four list
      name_list = []
      op_list = []
      num_val_list = []
      nom_val_list = []
  
      for name in data.columns.values:
          if np.issubdtype(data.dtypes[name], np.signedinteger):
              for val in np.unique(data.loc[:, name]):
                  name_list.append(
                      column_name_index_map[name]
                  ), op_list.append(EQ), num_val_list.append(
                      -1
                  ), nom_val_list.append(
                      val
                  )
  
          elif np.issubdtype(data.dtypes[name], np.floating):
              for val in np.unique(data.loc[:, name]):
                  name_list.append(
                      column_name_index_map[name]
                  ), op_list.append(GE), num_val_list.append(
                      val
                  ), nom_val_list.append(
                      -1
                  )
                  name_list.append(
                      column_name_index_map[name]
                  ), op_list.append(LE), num_val_list.append(
                      val
                  ), nom_val_list.append(
                      -1
                  )
          else:
              raise TypeError(
                  "unsupported type {type}".format(
                      type=data.dtypes[name]
                  )
              )
  
      return [
          np.array(name_list, dtype=np.int64),
          np.array(op_list, dtype=np.uint8),
          np.array(num_val_list, dtype=np.float64),
          np.array(nom_val_list, dtype=np.int64),
      ]
  
file_path: AIX360/aix360/algorithms/rule_induction/ripper/base.py
imports: import sys
  import numpy
  from pandas import DataFrame
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  from aix360.algorithms.rule_induction.ripper.binding import EQ, LE, GE
label_desc: Use image node_id 1 for calling a global function with example usage: _encoding_for_parallel(data, column_name_index_map) and returns: list