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# numpy.argpartition

numpy.argpartition(a, kth, axis=-1, kind='introselect', order=None)

  Perform an indirect partition 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 partitioned order.

  Parameters:
    a array_like

      Array to sort.

    kth int or sequence of ints

      Element index to partition by. The k-th element will be in its final sorted position and all smaller elements will be moved before it and all larger elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of k-th it will partition all of them into their sorted position at once.

    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{‘introselect’}, optional

      Selection algorithm. Default is ‘introselect’

    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 partition a along the specified axis. If a is one-dimensional, `a[index_array]` yields a partitioned a. More generally, `np.take_along_axis(a, index_array, axis=axis)` always yields the partitioned a, irrespective of dimensionality.

  See also

  `partition`

    Describes partition algorithms used.

  `ndarray.partition`

    Inplace partition.

  `argsort`

    Full indirect sort.

  `take_along_axis`

    Apply `index_array` from argpartition to an array as if by calling partition.

  Notes

  The returned indices are not guaranteed to be sorted according to the values. Furthermore, the default selection algorithm `introselect` is unstable, and hence the returned indices are not guaranteed to be the earliest/latest occurrence of the element.

  `argpartition` works for real/complex inputs with nan values, see `partition` for notes on the enhanced sort order and different selection algorithms.

  Examples

  One dimensional array:

      >>> import numpy as np
      >>> x = np.array([3, 4, 2, 1])
      >>> x[np.argpartition(x, 3)]
      array([2, 1, 3, 4]) # may vary
      >>> x[np.argpartition(x, (1, 3))]
      array([1, 2, 3, 4]) # may vary

      >>> x = [3, 4, 2, 1]
      >>> np.array(x)[np.argpartition(x, 3)]
      array([2, 1, 3, 4]) # may vary

  Multi-dimensional array:

      >>> x = np.array([[3, 4, 2], [1, 3, 1]])
      >>> index_array = np.argpartition(x, kth=1, axis=-1)
      >>> # below is the same as np.partition(x, kth=1)
      >>> np.take_along_axis(x, index_array, axis=-1)
      array([[2, 3, 4],
             [1, 1, 3]])