class_docstr: Interface for simple dictionary-based dataset format. The dictionary
  keys correspond to the column (i.e. dimension) names and the values
  are collections representing the values in that column.
class_docstr_tok: ["Interface","for","simple","dictionary-based","dataset","format",".","The","dictionary","keys","correspond","to","the","column","(","i.e",".","dimension",")","names","and","the","values","are","collections","representing","the","values","in","that","column","."]
code: def iloc(cls, dataset, index):
      rows, cols = index
      scalar = False
      if isscalar(cols):
          scalar = isscalar(rows)
          cols = [dataset.get_dimension(cols, strict=True)]
      elif isinstance(cols, slice):
          cols = dataset.dimensions()[cols]
      else:
          cols = [dataset.get_dimension(d, strict=True) for d in cols]
  
      if isscalar(rows):
          rows = [rows]
  
      new_data = {}
      for d, values in dataset.data.items():
          if d in cols:
              if isscalar(values):
                  new_data[d] = values
              else:
                  new_data[d] = values[rows]
  
      if scalar:
          arr = new_data[cols[0].name]
          return arr if isscalar(arr) else arr[0]
      return new_data
  
imports: from collections import OrderedDict, defaultdict
  import numpy
  from ..None import util
  from ..dimension import dimension_name
  from ..element import Element
  from ..ndmapping import NdMapping, item_check, sorted_context
  from ..util import isscalar
  from .interface import DataError, Interface
label_desc: Use image node_id 22 for calling the DictInterface obj's underlying member method code with example usage: obj.iloc(cls, dataset, index) and returns: new_data, unknown