class_docstr: Base class for advanced discretisation methods
docstr: Given one "original" feature (continuous), discretise it.
  
  Args:
      dataframe_one_column: dataframe with a single continuous feature, to be transformed into discrete
  Returns:
      Discrete feature, as an np.array of shape (len(df),)
docstr_tok: ["Given","one","``","original","''","feature","(","continuous",")",",","discretise","it",".","Args",":","dataframe_one_column",":","dataframe","with","a","single","continuous","feature",",","to","be","transformed","into","discrete","Returns",":","Discrete","feature",",","as","an","np.array","of","shape","(","len","(","df",")",",",")"]
code: def _transform_one_column(
      self, dataframe_one_column: pd.DataFrame
  ) -> np.array:
      """
      Given one "original" feature (continuous), discretise it.
  
      Args:
          dataframe_one_column: dataframe with a single continuous feature, to be transformed into discrete
      Returns:
          Discrete feature, as an np.array of shape (len(df),)
      """
      cols = list(dataframe_one_column.columns)
      if cols[0] in self.map_thresholds:
          split_points = self.map_thresholds[cols[0]]
          return np.digitize(
              dataframe_one_column.values.reshape(-1), split_points
          )
  
      if cols[0] not in self.feat_names:
          logging.warning(
              "%s is not in feat_names. The column is left unchanged",
              cols[0],
          )
      return dataframe_one_column.values.reshape(-1)
  
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 3 for calling the AbstractSupervisedDiscretiserMethod obj's underlying member method code with example usage: obj._transform_one_column(dataframe_one_column) and returns: dataframe_one_column, np