class_docstr: A data interface for public data. This class is an interface to DiCE explainers
  and contains methods to transform user-fed raw data into the format a DiCE explainer
  requires, and vice versa.
docstr: Normalizes continuous features to make them fall in the range [0,1].
code: def normalize_data(self, df):
      """Normalizes continuous features to make them fall in the range [0,1]."""
      result = df.copy()
      if isinstance(df, pd.DataFrame) or isinstance(df, dict):
          for feature_name in self.continuous_feature_names:
              max_value = self.data_df[feature_name].max()
              min_value = self.data_df[feature_name].min()
              if min_value == max_value:
                  result[feature_name] = 0
              else:
                  result[feature_name] = (
                      df[feature_name] - min_value
                  ) / (max_value - min_value)
      else:
          result = result.astype("float")
          for feature_index in self.continuous_feature_indexes:
              feature_name = self.feature_names[feature_index]
              max_value = self.data_df[feature_name].max()
              min_value = self.data_df[feature_name].min()
              if len(df.shape) == 1:
                  if min_value == max_value:
                      value = 0
                  else:
                      value = (df[feature_index] - min_value) / (
                          max_value - min_value
                      )
                  result[feature_index] = value
              else:
                  if min_value == max_value:
                      result[:, feature_index] = np.zeros(
                          len(df[:, feature_index])
                      )
                  else:
                      result[:, feature_index] = (
                          df[:, feature_index] - min_value
                      ) / (max_value - min_value)
      return result
  
imports: import logging
  from collections import defaultdict
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.preprocessing import LabelEncoder
  from dice_ml.data_interfaces.base_data_interface import _BaseData
  from dice_ml.utils.exception import SystemException
label_desc: Use image node_id 9 for calling the PublicData obj's underlying member method code with example usage: obj.normalize_data(df) and returns: result