docs: def _fit_transform_column(self, table, metadata, transformer_name, table_name):
          """Transform a column from table using transformer and given parameters.
  
          Args:
              table (pandas.DataFrame): Dataframe containing column to transform.
              metadata (dict): Metadata for given column.
              transformer_name (str): Name of transformer to use on column.
              table_name (str): Name of table in original dataset.
  
          Returns:
              pandas.DataFrame: Dataframe containing the transformed column. If self.missing=True,
                                it will contain a second column containing 0 and 1 marking if that
                                value was originally null or not.
          """
  
          column_name = metadata['name']
          content = {}
          columns = []
  
          if self.missing and table[column_name].isnull().any():
              null_transformer = transformers.NullTransformer(metadata)
              clean_column = null_transformer.fit_transform(table[column_name])
              null_name = '?' + column_name
              columns.append(null_name)
              content[null_name] = clean_column[null_name].values
              table[column_name] = clean_column[column_name]
  
          transformer_class = self.get_class(transformer_name)
          transformer = transformer_class(metadata)
  
          self.transformers[(table_name, column_name)] = transformer
          content[column_name] = transformer.fit_transform(table)[column_name].values
  
          columns = [column_name] + columns
          return pd.DataFrame(content, columns=columns)