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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 : list, optional When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified.
  Returns: | index_array : ndarray, int Array of indices that sort a along the specified axis. In other words, `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])