numpy.argsort(a, axis=-1, kind=None, order=None, *, stable=None, descending=<novalue>)
Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order.
Parameters:aarray_likeArray to sort.
axisint or None, optionalAxis along which to sort. The default is -1 (the last axis). If None, the flattened array is used.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optionalPlease use the stable parameter instead. This argument is retained for backwards compatibility and provides no additional control. ‘quicksort’ and ‘heapsort’ are equivalent to stable=False, while ‘mergesort’ and ‘stable’ are equivalent to stable=True.
orderstr or list of str, optionalWhen a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
stablebool, optionalSort stability. If True, the returned array will maintain the relative order of a values which compare as equal. If False or None, this is not guaranteed. Internally, this option selects kind='stable'. Default: None.
Added in version 2.0.0.
descendingbool, optionalSort order. If True, the returned array will be sorted in descending order. If False or None, the returned array will be sorted in ascending order. Values that are NaN are sorted to the end for both orders. Default: None.
Added in version 2.5.0.
Returns:index_arrayndarray, intArray of indices that sort a along the specified axis. If a is one-dimensional, a[index_array] yields a sorted a. More generally, np.take_along_axis(a,index_array,axis=axis) always yields the sorted a, irrespective of dimensionality.
Notes
See
for notes on the different sorting algorithms.
As of NumPy 1.4.0
works with real/complex arrays containing nan values. The enhanced sort order is documented in
.
Examples
One dimensional array:
>>> importnumpyasnp>>> x=np.array([3,1,2])>>> np.argsort(x)array([1, 2, 0])Two-dimensional array:
>>> x=np.array([[0,3],[2,2]])>>> xarray([[0, 3], [2, 2]])>>> ind=np.argsort(x,axis=0)# sorts along first axis (down)>>> indarray([[0, 1], [1, 0]])>>> np.take_along_axis(x,ind,axis=0)# same as np.sort(x, axis=0)array([[0, 2], [2, 3]])>>> ind=np.argsort(x,axis=1)# sorts along last axis (across)>>> indarray([[0, 1], [0, 1]])>>> np.take_along_axis(x,ind,axis=1)# same as np.sort(x, axis=1)array([[0, 3], [2, 2]])Indices of the sorted elements of an N-dimensional array:
>>> ind=np.unravel_index(np.argsort(x,axis=None),x.shape)>>> ind(array([0, 1, 1, 0]), array([0, 0, 1, 1]))>>> x[ind]# same as np.sort(x, axis=None)array([0, 2, 2, 3])Sorting with keys:
>>> x=np.array([(1,0),(0,1)],dtype=[('x','<i4'),('y','<i4')])>>> xarray([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])>>> np.argsort(x,order=('x','y'))array([1, 0])>>> np.argsort(x,order=('y','x'))array([0, 1])