numpy.lexsort

numpy.msort

numpy.argsort¶

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

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: a : array_like Array to sort. axis : int or None, optional Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. kind : {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with data type. The ‘mergesort’ option is retained for backwards compatibility. Changed in version 1.15.0.: The ‘stable’ option was added. order : str or list of str, optional When 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. index_array : ndarray, int Array 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.

`sort`
Describes sorting algorithms used.
`lexsort`
Indirect stable sort with multiple keys.
`ndarray.sort`
Inplace sort.
`argpartition`
Indirect partial sort.

Notes

See `sort` for notes on the different sorting algorithms.

As of NumPy 1.4.0 `argsort` works with real/complex arrays containing nan values. The enhanced sort order is documented in `sort`.

Examples

One dimensional array:

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

Two-dimensional array:

```>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
```
```>>> ind = np.argsort(x, axis=0)  # sorts along first axis (down)
>>> 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 (across)
>>> 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]])
```

Indices of the sorted elements of a 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')])
>>> x
array([(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])
```