dpnp.argsort

dpnp.argsort(a, axis=-1, kind=None, order=None)[source]

Returns the indices that would sort an array.

For full documentation refer to numpy.argsort.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- 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 ({None, "stable"}, optional) -- Default is None, which is equivalent to "stable". Unlike in NumPy any other options are not accepted here.

Returns:

out -- Array of indices that sort a along the specified axis. If a is one-dimensional, a[index_array] yields a sorted a. More generally, dpnp.take_along_axis(a, index_array, axis=axis) always yields the sorted a, irrespective of dimensionality. The return array has default array index data type.

Return type:

dpnp.ndarray

Notes

For zero-dimensional arrays, if axis=None, output is a one-dimensional array with a single zero element. Otherwise, an AxisError is raised.

Limitations

Parameters order is only supported with its default value. Parameters kind can only be None or "stable" which are equivalent. Otherwise NotImplementedError exception will be raised.

See also

dpnp.ndarray.argsort: Equivalent method.
dpnp.sort: Return a sorted copy of an array.
dpnp.lexsort: Indirect stable sort with multiple keys.
dpnp.argpartition: Indirect partial sort.
dpnp.take_along_axis: Apply index_array from obj:dpnp.argsort to an array as if by calling sort.

Examples

>>> import dpnp as np
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])

>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
       [2, 2]])

>>> ind = np.argsort(x, axis=0)  # sorts along first axis
>>> ind
array([[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
>>> ind
array([[0, 1],
       [0, 1]])
>>> np.take_along_axis(x, ind, axis=1)  # same as np.sort(x, axis=1)
array([[0, 3],
       [2, 2]])