numpy.lexsort¶

numpy.
lexsort
(keys, axis=1)¶ Perform an indirect sort using a sequence of keys.
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 secondtolast 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 : (N,) ndarray of ints
Array of indices that sort the keys along the specified axis.
See also
argsort
 Indirect sort.
ndarray.sort
 Inplace 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 >>> print(ind) [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 ofb
.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])