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# numpy.argsort

`numpy.``argsort`(a, axis=-1, kind='quicksort', order=None)

  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’}, optional Sorting algorithm. 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.
  Returns: | 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.

  See also

  `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]])

      >>> np.argsort(x, axis=0)
      array([[0, 1],
             [1, 0]])

      >>> np.argsort(x, axis=1)
      array([[0, 1],
             [0, 1]])

  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])