scipy.cluster.hierarchy.

leaves_list

scipy.cluster.hierarchy.leaves_list(Z)

Return a list of leaf node ids.

The return corresponds to the observation vector index as it appears in the tree from left to right. Z is a linkage matrix.

Parameters:

Zndarray

The hierarchical clustering encoded as a matrix. Z is a linkage matrix. See linkage for more information.

Returns:

leaves_listndarray

The list of leaf node ids.

See also

dendrogram

for information about dendrogram structure.

Notes

leaves_list has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	✅	⛔
JAX	✅	⛔
Dask	⚠️ merges chunks	n/a

See Support for the array API standard for more information.

Examples

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>>> from scipy.cluster.hierarchy import ward, dendrogram, leaves_list
>>> from scipy.spatial.distance import pdist
>>> from matplotlib import pyplot as plt

>>> X = [[0, 0], [0, 1], [1, 0],
...      [0, 4], [0, 3], [1, 4],
...      [4, 0], [3, 0], [4, 1],
...      [4, 4], [3, 4], [4, 3]]

>>> Z = ward(pdist(X))

The linkage matrix Z represents a dendrogram, that is, a tree that encodes the structure of the clustering performed. scipy.cluster.hierarchy.leaves_list shows the mapping between indices in the X dataset and leaves in the dendrogram:

>>> leaves_list(Z)
array([ 2,  0,  1,  5,  3,  4,  8,  6,  7, 11,  9, 10], dtype=int32)

>>> fig = plt.figure(figsize=(25, 10))
>>> dn = dendrogram(Z)
>>> plt.show()

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