code: def cat_transform(dfx, kpix, kpi1):
      """
      Encoding string features.
  
      Args
      ----
  
      dfx : dataframe
          The inputs data dataframe.
  
      kpix : string
          The column of the feature.
  
      kpi1 : list
          The list of feature names.
  
      Returns
      -------
      dfx : DataFrame
          The updated dataframe containing the encoded data.
  
      kpi1 : list
          The updated feature names containing the new dummy feature names.
      """
      df_dummy = pd.get_dummies(dfx[kpix].values)
      new_col_names = ["%s_%s" % (kpix, x) for x in df_dummy.columns]
      df_dummy.columns = new_col_names
      dfx = pd.concat([dfx, df_dummy], axis=1)
      for new_col in new_col_names:
          if new_col not in kpi1:
              kpi1.append(new_col)
      if kpix in kpi1:
          kpi1.remove(kpix)
      return dfx, kpi1
  
file_path: causalml/causalml/inference/tree/utils.py
imports: import time
  from typing import Callable
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: cat_transform(dfx, kpix, kpi1) and returns: dfx,  kpi1