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# skbio.stats.distance.DistanceMatrix

class `skbio.stats.distance.``DistanceMatrix`(data, ids=None)

  Store distances between objects.

  A DistanceMatrix is a DissimilarityMatrix with the additional requirement that the matrix data is symmetric. There are additional methods made available that take advantage of this symmetry.

  See also

  `DissimilarityMatrix`

  Notes

  The distances are stored in redundant (square-form) format [1]. To facilitate use with other scientific Python routines (e.g., scipy), the distances can be retrieved in condensed (vector-form) format using condensed_form.

  DistanceMatrix only requires that the distances it stores are symmetric. Checks are not performed to ensure the other three metric properties hold (non-negativity, identity of indiscernibles, and triangle inequality) [2]. Thus, a DistanceMatrix instance can store distances that are not metric.

  References

  [1] | http://docs.scipy.org/doc/scipy/reference/spatial.distance.html

  [2] | http://planetmath.org/metricspace

  Attributes

  `T` | Transpose of the dissimilarity matrix.
  `data` | Array of dissimilarities.
  `default_write_format` |
  `dtype` | Data type of the dissimilarities.
  `ids` | Tuple of object IDs.
  `png` | Display heatmap in IPython Notebook as PNG.
  `shape` | Two-element tuple containing the dissimilarity matrix dimensions.
  `size` | Total number of elements in the dissimilarity matrix.
  `svg` | Display heatmap in IPython Notebook as SVG.

  Built-ins

  `x in dm` | Check if the specified ID is in the dissimilarity matrix.
  `dm1 == dm2` | Compare this dissimilarity matrix to another for equality.
  `dm[x]` | Slice into dissimilarity data by object ID or numpy indexing.
  `dm1 != dm2` | Determine whether two dissimilarity matrices are not equal.
  `str(dm)` | Return a string representation of the dissimilarity matrix.

  Methods

  `between`(from_, to_[, allow_overlap]) | Obtain the distances between the two groups of IDs
  `condensed_form`() | Return an array of distances in condensed format.
  `copy`() | Return a deep copy of the dissimilarity matrix.
  `filter`(ids[, strict]) | Filter the dissimilarity matrix by IDs.
  `from_iterable`(iterable, metric[, key, keys, …]) | Create DistanceMatrix from all pairs in an iterable given a metric.
  `index`(lookup_id) | Return the index of the specified ID.
  `permute`([condensed]) | Randomly permute both rows and columns in the matrix.
  `plot`([cmap, title]) | Creates a heatmap of the dissimilarity matrix
  `read`(file[, format]) | Create a new `DistanceMatrix` instance from a file.
  `redundant_form`() | Return an array of dissimilarities in redundant format.
  `to_data_frame`() | Create a `pandas.DataFrame` from this `DissimilarityMatrix`.
  `to_series`() | Create a `pandas.Series` from this `DistanceMatrix`.
  `transpose`() | Return the transpose of the dissimilarity matrix.
  `within`(ids) | Obtain all the distances among the set of IDs
  `write`(file[, format]) | Write an instance of `DistanceMatrix` to a file.