code: def process_one_column(self, col_vals, col_name):
      ignore_none_fields = False
      if self.get("transformed_column_descriptions", None) is not None:
          ignore_none_fields = True
      column_descriptions = self.get(
          "transformed_column_descriptions", self.column_descriptions
      )
  
      col_desc = column_descriptions.get(col_name)
  
      # This is what we do to columns that were not present at fitting time.
      # All columns that were present at fitting time that had no entry in column_descriptions were filled in with 'continuous'
      if col_desc is None:
          result = {}
  
      elif col_desc == "categorical":
          # We will handle categorical data later, one-hot-encoding it inside DataFrameVectorizer (or LabelEncoding it for lgbm)
          result = {col_name: col_vals}
  
      elif col_desc in (
          None,
          "continuous",
          "numerical",
          "float",
          "int",
      ):
          # For all of our numerical columns, try to turn all of these values into floats
          # This function handles commas inside strings that represent numbers, and returns nan if we cannot turn this value into a float. nans are ignored in DataFrameVectorizer
          try:
              col_vals = col_vals.apply(
                  lambda x: clean_val_nan_version(
                      col_name, x, replacement_val=0
                  )
              )
              result = {col_name: col_vals}
          except TypeError as e:
              raise (e)
          except UnicodeEncodeError as e:
              print(
                  "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
              )
              print(
                  "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
              )
              print(
                  "We have found a column that is not marked as a categorical column that has unicode values in it"
              )
              print("Here is the column name:")
              print(col_name)
              print(
                  "The actual value that caused the issue is logged right above the exclamation points"
              )
              print(
                  "Please either mark this column as categorical, or clean up the values in this column"
              )
              print(
                  "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
              )
              print(
                  "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
              )
  
      elif col_desc == "date":
          result = add_date_features_df(col_vals, col_name)
  
      elif col_name in self.text_columns:
          col_names = self.text_columns[col_name].cleaned_feature_names
  
          col_vals.fillna("nan", inplace=True)
          if pandas_version < "0.20.0":
              nlp_matrix = self.text_columns[col_name].transform(
                  col_vals.astype(str, raise_on_error=False)
              )
          else:
              nlp_matrix = self.text_columns[col_name].transform(
                  col_vals.astype(str, errors="ignore")
              )
  
          nlp_matrix = nlp_matrix.toarray()
  
          text_df = pd.DataFrame(nlp_matrix)
          text_df.columns = col_names
  
          result = {}
          for col_vals in text_df.columns:
              result[col_vals] = text_df[col_vals].astype(int)
  
      elif col_desc in self.vals_to_drop:
          result = {}
      else:
          # If we have gotten here, the value is not any that we recognize
          # This is most likely a typo that the user would want to be informed of, or a case while we're developing on auto_ml itself.
          # In either case, it's useful to log it.
          print(
              "When transforming the data, we have encountered a value in column_descriptions that is not currently supported. The column has been dropped to allow the rest of the pipeline to run. Here's the name of the column:"
          )
          print(col_name)
          print(
              "And here is the value for this column passed into column_descriptions:"
          )
          print(col_desc)
          warnings.warn(
              "UnknownValueInColumnDescriptions: Please make sure all the values you pass into column_descriptions are valid."
          )
          result = {}
  
      return result
  
imports: import datetime
  import os
  import dateutil
  import numpy
  import pandas
  from pandas import __version__
  import pathos
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_extraction.text import TfidfVectorizer
  import warnings
label_desc: Use image node_id 5 for calling the BasicDataCleaning obj's underlying member method code with example usage: obj.process_one_column(col_vals, col_name) and returns: result