class_docstr: `ConcatedEncodedDs` abstracts over multiple encoded datasources (`EncodedDs`) as if they were a single entity.
class_docstr_tok: ["`","ConcatedEncodedDs","`","abstracts","over","multiple","encoded","datasources","(","`","EncodedDs","`",")","as","if","they","were","a","single","entity","."]
docstr: See `lightwood.data.encoded_ds.EncodedDs.get_column_original_data()`.
docstr_tok: ["See","`","lightwood.data.encoded_ds.EncodedDs.get_column_original_data","(",")","`","."]
code: def get_column_original_data(self, column_name: str) -> pd.Series:
      """
      See `lightwood.data.encoded_ds.EncodedDs.get_column_original_data()`.
      """
      encoded_df_arr = [
          x.get_column_original_data(column_name)
          for x in self.encoded_ds_arr
      ]
      return pd.concat(encoded_df_arr)
  
imports: import inspect
  from typing import List, Tuple, Dict
  import torch
  import numpy
  import pandas
  from torch.utils.data import Dataset
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 4 for calling the ConcatedEncodedDs obj's underlying member method code with example usage: obj.get_column_original_data(column_name) and returns: pd