class_docstr: Base class for advanced discretisation methods
docstr: Given one "original" dataframe, discretise it.
  
  Args:
      data: dataframe with continuous features, to be transformed into discrete
  Returns:
      discretised version of the input data
docstr_tok: ["Given","one","``","original","''","dataframe",",","discretise","it",".","Args",":","data",":","dataframe","with","continuous","features",",","to","be","transformed","into","discrete","Returns",":","discretised","version","of","the","input","data"]
code: def transform(self, data: pd.DataFrame) -> np.array:
      """
      Given one "original" dataframe, discretise it.
  
      Args:
          data: dataframe with continuous features, to be transformed into discrete
      Returns:
          discretised version of the input data
      """
      outputs = {}
      for col in data.columns:
          outputs[col] = self._transform_one_column(data[[col]])
  
      transformed_df = pd.DataFrame.from_dict(outputs)
  
      return transformed_df.set_index(data.index)
  
file_path: causalnex/causalnex/discretiser/abstract_discretiser.py
imports: import logging
  from abc import ABC, abstractmethod
  from typing import List
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 4 for calling the AbstractSupervisedDiscretiserMethod obj's underlying member method code with example usage: obj.transform(data) and returns: transformed_df