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, its rows are interpreted as the sorting keys and sorting is according to the last row, second last row etc.
The k different "columns" to be sorted. The last column (or row if keys
is a 2D array) is the primary sort key.
Axis to be indirectly sorted. By default, sort over the last axis.
Array of indices that sort the keys along the specified axis.
Perform an indirect stable sort using a sequence of keys.
argsort
Indirect sort.
ndarray.sort
In-place sort.
sort
Return a sorted copy of an array.
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])See :
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