original_docstring: 
      Preprocessing on the input feature importance dataframe
  
      :param imp_df: feature importance pandas dataframe
      :param imp_name: column name of the importance value
      :return:
          dataframe with relative_imp and feat_rank
      
docstring: Preprocessing on the input feature importance dataframe
docstring_tokens: Preprocessing on the input feature importance dataframe
code: def _process_imp(imp_df, imp_name):
      """
      Preprocessing on the input feature importance dataframe
  
      :param imp_df: feature importance pandas dataframe
      :param imp_name: column name of the importance value
      :return:
          dataframe with relative_imp and feat_rank
      """
  
      imp_df = imp_df.sort_values(
          by=imp_name,
          ascending=False).reset_index(
          drop=True)
      imp_df['relative_imp'] = imp_df[imp_name] * 1.0 / imp_df[imp_name].max()
      imp_df['relative_imp'] = imp_df['relative_imp'].apply(
          lambda x: round(x, 3))
      imp_df['feat_rank'] = imp_df.index.values + 1
      return imp_df
code_tokens: def _process_imp ( imp_df , imp_name ) : imp_df = imp_df . sort_values ( by = imp_name , ascending = False ) . reset_index ( drop = True ) imp_df [ 'relative_imp' ] = imp_df [ imp_name ] * 1.0 / imp_df [ imp_name ] . max ( ) imp_df [ 'relative_imp' ] = imp_df [ 'relative_imp' ] . apply ( lambda x : round ( x , 3 ) ) imp_df [ 'feat_rank' ] = imp_df . index . values + 1 return imp_df
short_docstring: Preprocessing on the input feature importance dataframe
short_docstring_tokens: Preprocessing on the input feature importance dataframe
comment: """
      Preprocessing on the input feature importance dataframe
  
      :param imp_df: feature importance pandas dataframe
      :param imp_name: column name of the importance value
      :return:
          dataframe with relative_imp and feat_rank
      """