class_docstr: Convert data from a columnar representation like Pandas and Numpy into values appropriate for
  element_data.py types.
  
  Args:
      name (str): the name of the argument for error messages
      default_type (hashable): the default type to use for data without a type
      type_column (hashable, optional): the name of the type column, if one is being used
      column_defaults (dict of hashable to any): any default values for columns (using names before renaming!)
      selected_columns (dict of hashable to hashable): renamings for columns, mapping original name to new name
      dtype (str or numpy dtype): the data type to use for the feature matrices
      transform_columns (dict of hashable to callable): column transformations, maps column name to transform
class_docstr_tok: ["Convert","data","from","a","columnar","representation","like","Pandas","and","Numpy","into","values","appropriate","for","element_data.py","types",".","Args",":","name","(","str",")",":","the","name","of","the","argument","for","error","messages","default_type","(","hashable",")",":","the","default","type","to","use","for","data","without","a","type","type_column","(","hashable",",","optional",")",":","the","name","of","the","type","column",",","if","one","is","being","used","column_defaults","(","dict","of","hashable","to","any",")",":","any","default","values","for","columns","(","using","names","before","renaming","!",")","selected_columns","(","dict","of","hashable","to","hashable",")",":","renamings","for","columns",",","mapping","original","name","to","new","name","dtype","(","str","or","numpy","dtype",")",":","the","data","type","to","use","for","the","feature","matrices","transform_columns","(","dict","of","hashable","to","callable",")",":","column","transformations",",","maps","column","name","to","transform"]
code: def name(self, type_name=None):
      if type_name is None:
          return self._parent_name
      return f"{self._parent_name}[{type_name!r}]"
  
imports: from collections import defaultdict, namedtuple
  from typing import Iterable
  import warnings
  import numpy
  import pandas
  from ..globalvar import SOURCE, TARGET, WEIGHT, TYPE_ATTR_NAME
  from .element_data import NodeData, EdgeData
  from .indexed_array import IndexedArray
  from .validation import comma_sep, require_dataframe_has_columns
  from .utils import is_real_iterable, zero_sized_array, smart_array_concatenate, smart_array_index
label_desc: Use image node_id 2 for calling the ColumnarConverter obj's underlying member method code with example usage: obj.name(type_name) and returns: self, str, type_name, str, self