class_docstr: A tabular element type to allow convenient visualization of either
  a standard Python dictionary or a list of tuples
  (i.e. input suitable for an dict constructor).
  Tables store heterogeneous data with different labels.
  
  Dimension objects are also accepted as keys, allowing dimensional
  information (e.g. type and units) to be associated per heading.
class_docstr_tok: ["A","tabular","element","type","to","allow","convenient","visualization","of","either","a","standard","Python","dictionary","or","a","list","of","tuples","(","i.e",".","input","suitable","for","an","dict","constructor",")",".","Tables","store","heterogeneous","data","with","different","labels",".","Dimension","objects","are","also","accepted","as","keys",",","allowing","dimensional","information","(","e.g",".","type","and","units",")","to","be","associated","per","heading","."]
code: def __init__(self, data, **params):
      if data is None:
          data = []
      if isinstance(data, dict):
          pass
      elif isinstance(data, list):
          data = dict(data)
      else:
          data = dict(list(data))
      if "vdims" not in params:
          params["vdims"] = list(data.keys())
      str_keys = dict((dimension_name(k), v) for (k, v) in data.items())
      super().__init__(str_keys, **params)
  
imports: import numpy
  import param
  from ..core import Dataset, Element, Tabular
  from ..core.dimension import Dimension, dimension_name
  from .selection import SelectionIndexExpr
label_desc: Use image node_id 3 to create a new ItemTable object from inherited base classes: Element with example: obj = ItemTable(data)