numpy.sort¶
- numpy.sort(a, axis=-1, kind='quicksort', order=None)[source]¶
Return a sorted copy of an array.
Parameters: a : array_like
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional
Sorting algorithm. Default is ‘quicksort’.
order : list, optional
When a is a structured array, this argument specifies which fields to compare first, second, and so on. This list does not need to include all of the fields.
Returns: sorted_array : ndarray
Array of the same type and shape as a.
See also
- ndarray.sort
- Method to sort an array in-place.
- argsort
- Indirect sort.
- lexsort
- Indirect stable sort on multiple keys.
- searchsorted
- Find elements in a sorted array.
- partition
- Partial sort.
Notes
The various sorting algorithms are characterized by their average speed, worst case performance, work space size, and whether they are stable. A stable sort keeps items with the same key in the same relative order. The three available algorithms have the following properties:
kind speed worst case work space stable ‘quicksort’ 1 O(n^2) 0 no ‘mergesort’ 2 O(n*log(n)) ~n/2 yes ‘heapsort’ 3 O(n*log(n)) 0 no All the sort algorithms make temporary copies of the data when sorting along any but the last axis. Consequently, sorting along the last axis is faster and uses less space than sorting along any other axis.
The sort order for complex numbers is lexicographic. If both the real and imaginary parts are non-nan then the order is determined by the real parts except when they are equal, in which case the order is determined by the imaginary parts.
Previous to numpy 1.4.0 sorting real and complex arrays containing nan values led to undefined behaviour. In numpy versions >= 1.4.0 nan values are sorted to the end. The extended sort order is:
- Real: [R, nan]
- Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]
where R is a non-nan real value. Complex values with the same nan placements are sorted according to the non-nan part if it exists. Non-nan values are sorted as before.
Examples
>>> a = np.array([[1,4],[3,1]]) >>> np.sort(a) # sort along the last axis array([[1, 4], [1, 3]]) >>> np.sort(a, axis=None) # sort the flattened array array([1, 1, 3, 4]) >>> np.sort(a, axis=0) # sort along the first axis array([[1, 1], [3, 4]])
Use the order keyword to specify a field to use when sorting a structured array:
>>> dtype = [('name', 'S10'), ('height', float), ('age', int)] >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ... ('Galahad', 1.7, 38)] >>> a = np.array(values, dtype=dtype) # create a structured array >>> np.sort(a, order='height') array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41), ('Lancelot', 1.8999999999999999, 38)], dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
Sort by age, then height if ages are equal:
>>> np.sort(a, order=['age', 'height']) array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38), ('Arthur', 1.8, 41)], dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])