# jax.numpy.lexsort¶

jax.numpy.lexsort(keys, axis=-1)[source]

Perform an indirect stable sort using a sequence of keys.

LAX-backend implementation of lexsort(). Original docstring below.

lexsort(keys, axis=-1)

Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D array is provided for the keys argument, it’s rows are interpreted as the sorting keys and sorting is according to the last row, second last row etc.

Parameters
• keys ((k, N) array or tuple containing k (N,)-shaped sequences) – The k different “columns” to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.

• axis (int, optional) – Axis to be indirectly sorted. By default, sort over the last axis.

Returns

indices – Array of indices that sort the keys along the specified axis.

Return type

(N,) ndarray of ints

argsort()

Indirect sort.

ndarray.sort()

In-place sort.

sort()

Return a sorted copy of an array.

Examples

Sort names: first by surname, then by name.

>>> surnames =    ('Hertz',    'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])

>>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']


Sort two columns of numbers:

>>> a = [1,5,1,4,3,4,4] # First column
>>> b = [9,4,0,4,0,2,1] # Second column
>>> ind = np.lexsort((b,a)) # Sort by a, then by b
>>> ind
array([2, 0, 4, 6, 5, 3, 1])

>>> [(a[i],b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]


Note that sorting is first according to the elements of a. Secondary sorting is according to the elements of b.

A normal argsort would have yielded:

>>> [(a[i],b[i]) for i in np.argsort(a)]
[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]


Structured arrays are sorted lexically by argsort:

>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
...              dtype=np.dtype([('x', int), ('y', int)]))

>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])