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# skbio.stats.distance.DissimilarityMatrix.within

`DissimilarityMatrix.``within`(ids)

  Obtain all the distances among the set of IDs

  State: Experimental as of 0.5.5.

  Parameters: | ids (Iterable of str) – The IDs to obtain distances for. All pairs of distances are returned such that, if provided [‘a’, ‘b’, ‘c’], the distances for [(‘a’, ‘a’), (‘a’, ‘b’), (‘a’, ‘c’), (‘b’, ‘a’), (‘b’, ‘b’), (‘b’, ‘c’), (‘c’, ‘a’), (‘c’, ‘b’), (‘c’, ‘c’)] are gathered.
  Returns: | (i, j, value) representing the source ID (“i”), the target ID (“j”) and the distance (“value”).
  Return type: | pd.DataFrame
  Raises: | `MissingIDError` – If an ID(s) specified is not in the dissimilarity matrix.

  Notes

  Order of the return items is stable, meaning that requesting IDs [‘a’, ‘b’] is equivalent to [‘b’, ‘a’]. The order is with respect to the order of the .ids attribute of self.

  Example

      >>> from skbio.stats.distance import DissimilarityMatrix
      >>> dm = DissimilarityMatrix([[0, 1, 2, 3, 4], [1, 0, 1, 2, 3],
      ...                           [2, 1, 0, 1, 2], [3, 2, 1, 0, 1],
      ...                           [4, 3, 2, 1, 0]],
      ...                          ['A', 'B', 'C', 'D', 'E'])
      >>> dm.within(['A', 'B', 'C'])
         i  j  value
      0  A  A    0.0
      1  A  B    1.0
      2  A  C    2.0
      3  B  A    1.0
      4  B  B    0.0
      5  B  C    1.0
      6  C  A    2.0
      7  C  B    1.0
      8  C  C    0.0