class_docstr: The feature type for the constructor of DFtoVW
class_docstr_tok: ["The","feature","type","for","the","constructor","of","DFtoVW"]
docstr: Returns the Feature string representation.
  
  Args:
      df: The dataframe from which to select the column(s).
  
  Returns:
      The Feature string representation.
docstr_tok: ["Returns","the","Feature","string","representation",".","Args",":","df",":","The","dataframe","from","which","to","select","the","column","(","s",")",".","Returns",":","The","Feature","string","representation","."]
code: def process(
      self, df: pd.DataFrame, ensure_valid_values=True
  ) -> pd.Series:
      """Returns the Feature string representation.
  
      Args:
          df: The dataframe from which to select the column(s).
  
      Returns:
          The Feature string representation.
      """
      value_col = self.value.get_col(df)
      if self.as_type:
          sep = ":" if self.as_type == "numerical" else "="
      else:
          sep = ":" if self.value.is_number(df) else "="
  
      if ensure_valid_values:
          value_col = value_col.apply(_Col.make_valid_name)
  
      out = value_col.where(
          value_col == "", self.name + sep + value_col
      )
      return out
  
imports: import warnings
  import numpy
  import pandas
  from typing import Any, Hashable, List, Optional, Tuple, Type, Union
label_desc: Use image node_id 2 for calling the Feature obj's underlying member method code with example usage: obj.process(df, ensure_valid_values) and returns: out