class_docstr: A data interface for private data with meta information.
class_docstr_tok: ["A","data","interface","for","private","data","with","meta","information","."]
docstr: De-normalizes continuous features from [0,1] range to original range.
docstr_tok: ["De-normalizes","continuous","features","from","[","0,1","]","range","to","original","range","."]
code: def de_normalize_data(self, df):
      """De-normalizes continuous features from [0,1] range to original range."""
      if len(df) == 0:
          return df
      result = df.copy()
      for feature_name in self.continuous_feature_names:
          max_value = self.permitted_range[feature_name][1]
          min_value = self.permitted_range[feature_name][0]
          result[feature_name] = (
              df[feature_name] * (max_value - min_value)
          ) + min_value
      return result
  
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 6 for calling the PrivateData obj's underlying member method code with example usage: obj.de_normalize_data(df) and returns: result, df