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# scipy.cluster.hierarchy.to_tree

`scipy.cluster.hierarchy.``to_tree`(Z, rd=False)

  Converts a linkage matrix into an easy-to-use tree object.

  The reference to the root `ClusterNode` object is returned (by default).

  Each `ClusterNode` object has a `left`, `right`, `dist`, `id`, and `count` attribute. The left and right attributes point to ClusterNode objects that were combined to generate the cluster. If both are None then the `ClusterNode` object is a leaf node, its count must be 1, and its distance is meaningless but set to 0.

  Note: This function is provided for the convenience of the library user. ClusterNodes are not used as input to any of the functions in this library.

  Parameters: | Z : ndarray The linkage matrix in proper form (see the `linkage` function documentation). rd : bool, optional When False (default), a reference to the root `ClusterNode` object is returned. Otherwise, a tuple `(r, d)` is returned. `r` is a reference to the root node while `d` is a list of `ClusterNode` objects - one per original entry in the linkage matrix plus entries for all clustering steps. If a cluster id is less than the number of samples `n` in the data that the linkage matrix describes, then it corresponds to a singleton cluster (leaf node). See `linkage` for more information on the assignment of cluster ids to clusters.
  Returns: | tree : ClusterNode or tuple (ClusterNode, list of ClusterNode) If `rd` is False, a `ClusterNode`. If `rd` is True, a list of length `2*n - 1`, with `n` the number of samples. See the description of rd above for more details.

  See also

  `linkage`, `is_valid_linkage`, `ClusterNode`

  Examples

      >>> from scipy.cluster import hierarchy
      >>> x = np.random.rand(10).reshape(5, 2)
      >>> Z = hierarchy.linkage(x)
      >>> hierarchy.to_tree(Z)
      <scipy.cluster.hierarchy.ClusterNode object at ...
      >>> rootnode, nodelist = hierarchy.to_tree(Z, rd=True)
      >>> rootnode
      <scipy.cluster.hierarchy.ClusterNode object at ...
      >>> len(nodelist)
      9