class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: Transforms query_instance into one-hot-encoded and min-max normalized data. query_instance should be a dict,
  a dataframe, a list, or a list of dicts
docstr_tok: ["Transforms","query_instance","into","one-hot-encoded","and","min-max","normalized","data",".","query_instance","should","be","a","dict",",","a","dataframe",",","a","list",",","or","a","list","of","dicts"]
code: def get_ohe_min_max_normalized_data(self, query_instance):
      """Transforms query_instance into one-hot-encoded and min-max normalized data. query_instance should be a dict,
      a dataframe, a list, or a list of dicts"""
      query_instance = self.prepare_query_instance(query_instance)
      ohe_base_df = self.prepare_df_for_ohe_encoding()
      temp = pd.concat(
          [ohe_base_df, query_instance], ignore_index=True, sort=False
      )
      temp = self.one_hot_encode_data(temp)
      temp = temp.tail(query_instance.shape[0]).reset_index(drop=True)
      # returns a pandas dataframe
      return self.normalize_data(temp).apply(pd.to_numeric)
  
imports: import collections
  import logging
  import sys
  from collections import defaultdict
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
label_desc: Use image node_id 23 for calling the PrivateData obj's underlying member method code with example usage: obj.get_ohe_min_max_normalized_data(query_instance) and returns: self