numpy.sort#

numpy.sort(a,axis=-1,kind=None,order=None,*,stable=None)[source]#

Return a sorted copy of an array.

Parameters:

aarray_like

Array to be sorted.

axisint or None, optional

Axis along which to sort. If None, the array is flattened beforesorting. The default is -1, which sorts along the last axis.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional

Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’and ‘mergesort’ use timsort or radix sort under the covers and,in general, the actual implementation will vary with data type.The ‘mergesort’ option is retained for backwards compatibility.

orderstr or list of str, optional

Whena is an array with fields defined, this argument specifieswhich fields to compare first, second, etc. A single field canbe specified as a string, and not all fields need be specified,but unspecified fields will still be used, in the order in whichthey come up in the dtype, to break ties.

stablebool, optional

Sort stability. IfTrue, the returned array will maintainthe relative order ofa values which compare as equal.IfFalse orNone, this is not guaranteed. Internally,this option selectskind='stable'. Default:None.

New in version 2.0.0.

Returns:

sorted_arrayndarray

Array of the same type and shape asa.

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. Astable sort keeps items with the same key in the same relativeorder. The four algorithms implemented in NumPy have the followingproperties:

kind	speed	worst case	work space	stable
‘quicksort’	1	O(n^2)	0	no
‘heapsort’	3	O(n*log(n))	0	no
‘mergesort’	2	O(n*log(n))	~n/2	yes
‘timsort’	2	O(n*log(n))	~n/2	yes

Note

The datatype determines which of ‘mergesort’ or ‘timsort’is actually used, even if ‘mergesort’ is specified. User selectionat a finer scale is not currently available.

For performance,sort makes a temporary copy if needed to make the datacontiguousin memory along the sort axis. For even better performance and reducedmemory consumption, ensure that the array is already contiguous along thesort axis.

The sort order for complex numbers is lexicographic. If both the realand imaginary parts are non-nan then the order is determined by thereal parts except when they are equal, in which case the order isdetermined by the imaginary parts.

Previous to numpy 1.4.0 sorting real and complex arrays containing nanvalues led to undefined behaviour. In numpy versions >= 1.4.0 nanvalues 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 nanplacements are sorted according to the non-nan part if it exists.Non-nan values are sorted as before.

quicksort has been changed to:introsort.When sorting does not make enough progress it switches toheapsort.This implementation makes quicksort O(n*log(n)) in the worst case.

‘stable’ automatically chooses the best stable sorting algorithmfor the data type being sorted.It, along with ‘mergesort’ is currently mapped totimsortorradix sortdepending on the data type.API forward compatibility currently limits theability to select the implementation and it is hardwired for the differentdata types.

Timsort is added for better performance on already or nearlysorted data. On random data timsort is almost identical tomergesort. It is now used for stable sort while quicksort is still thedefault sort if none is chosen. For timsort details, refer toCPython listsort.txt‘mergesort’ and ‘stable’ are mapped to radix sort for integer data types.Radix sort is an O(n) sort instead of O(n log n).

NaT now sorts to the end of arrays for consistency with NaN.

Examples

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>>>importnumpyasnp>>>a=np.array([[1,4],[3,1]])>>>np.sort(a)# sort along the last axisarray([[1, 4],       [1, 3]])>>>np.sort(a,axis=None)# sort the flattened arrayarray([1, 1, 3, 4])>>>np.sort(a,axis=0)# sort along the first axisarray([[1, 1],       [3, 4]])

Use theorder keyword to specify a field to use when sorting astructured 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')])

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