text: from abc import ABC, abstractclassmethod
  
  import pandas as pd
  import parse
  from actymath import ActyMathError, columns
  
  
  class Column:
      """ An abstract class for columns to populate """
  
      default = None  # A default value to use if the value is missing
      parameters = {}  # Required kwargs parameters for populating this Column.
      column_name = "overwrite_me({kwarg})"
      dependencies = []  # List of column names required for calculation
  
      @classmethod
      def column(cls, **kwargs):
          """ Returns the string Column name to create in DataFrame. """
          return cls.column_name.format(**kwargs)
  
      @classmethod
      def parse_column(cls, column):
          """
          Reverses the column name into the column_name and kwargs.
          Returns them as a tuple (column_name, dict of kwargs).
          """
          result = parse.parse(cls.column_name, column, case_sensitive=True)
          if result:
              return (cls.column_name, result.named)
  
      @classmethod
      def calculate(cls, calc: pd.DataFrame, **kwargs):
          """
          Pandas calculation using dataframe columns.
          Must return a Pandas Series.
          This series will be inserted in the column with the column name.
          """
          raise ActyMathError(f"Calculate method for {cls} not yet implemented.")
  
      @classmethod
      def populate(cls, calc, force=False, **kwargs):
          """ Populates this object and any dependencies in the Grid DataFrame. """
  
          # Validate that the required parameters are provided
          for param in cls.parameters.keys():
              if param not in kwargs:
                  raise ActyMathError(
                      f"Missing parameter ({param}) to populate ({cls}) - {cls.parameters[param]}."
                  )
  
          # If column already exists then skip
          if cls.column(**kwargs) in calc.columns and force is False:
              return
  
          # First check dependencies exist and if not, then create them, first
          for column_name in cls.dependencies:
              column = column_name.format(**kwargs)
              if column not in calc.columns or force is True:
                  calc.populate(column, force=force)
                  # try:
                  #     class_ = calc.register[column_name]
                  #     class_.populate(calc, force=force, **kwargs)
                  # except Exception as e:
                  #     raise ActyMathError(
                  #         f"Unable to populate dependency column ({column_name}) - does the class for this column exist? - {e}"
                  #     )
  
          # Calculate pandas series and insert in a new column
          new_column = cls.column(**kwargs)
          calc[new_column] = cls.calculate(calc, **kwargs)
          if cls.default is not None:
              calc[new_column].fillna(0, inplace=True)
  
          return new_column
  