# 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.

  New in version 1.8.0.

  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 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.

      Deprecated since version 1.22.0: Passing booleans as index is deprecated.

    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)` 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

  See `partition` for notes on the different selection algorithms.

  Examples

  One dimensional array:

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

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

  Multi-dimensional array:

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

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