class_docstr: The DaskInterface allows a Dataset objects to wrap a dask
  DataFrame object. Using dask allows loading data lazily
  and performing out-of-core operations on the data, making
  it possible to work on datasets larger than memory.
  
  The DaskInterface covers almost the complete API exposed
  by the PandasInterface with two notable exceptions:
  
  1) Sorting is not supported and any attempt at sorting will
     be ignored with an warning.
  2) Dask does not easily support adding a new column to an existing
     dataframe unless it is a scalar, add_dimension will therefore
     error when supplied a non-scalar value.
  4) Not all functions can be easily applied to a dask dataframe so
     some functions applied with aggregate and reduce will not work.
docstr: Given a dataset object and data in the appropriate format for
  the interface, return a simple scalar.
docstr_tok: ["Given","a","dataset","object","and","data","in","the","appropriate","format","for","the","interface",",","return","a","simple","scalar","."]
code: def unpack_scalar(cls, dataset, data):
      """
      Given a dataset object and data in the appropriate format for
      the interface, return a simple scalar.
      """
      import dask.dataframe as dd
  
      if len(data.columns) > 1 or len(data) != 1:
          return data
      if isinstance(data, dd.DataFrame):
          data = data.compute()
      return data.iat[0, 0]
  
imports: import sys
  import numpy
  import pandas
  from ..None import util
  from ..dimension import Dimension
  from ..element import Element
  from ..ndmapping import NdMapping, item_check, sorted_context
  from .interface import Interface
  from .pandas import PandasInterface
label_desc: Use image node_id 14 for calling the DaskInterface obj's underlying member method code with example usage: obj.unpack_scalar(cls, dataset, data) and returns: data, data