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Fonction partition - module numpy

Signature de la fonction partition

def partition(a, kth, axis=-1, kind='introselect', order=None) 

Description

partition.__doc__

    Return a partitioned copy of an array.

    Creates a copy of the array with its elements rearranged in such a
    way that the value of the element in k-th position is in the
    position it would be in a sorted array. All elements smaller than
    the k-th element are moved before this element and all equal or
    greater are moved behind it. The ordering of the elements in the two
    partitions is undefined.

    .. versionadded:: 1.8.0

    Parameters
    ----------
    a : array_like
        Array to be sorted.
    kth : int or sequence of ints
        Element index to partition by. The k-th value of the element
        will be in its final sorted position and all smaller elements
        will be moved before it and all equal or greater 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 elements
        indexed by k-th  of them into their sorted position at once.
    axis : int or None, optional
        Axis along which to sort. If None, the array is flattened before
        sorting. The default is -1, which sorts along the last axis.
    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.  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
    -------
    partitioned_array : ndarray
        Array of the same type and shape as `a`.

    See Also
    --------
    ndarray.partition : Method to sort an array in-place.
    argpartition : Indirect partition.
    sort : Full sorting

    Notes
    -----
    The various selection algorithms are characterized by their average
    speed, worst case performance, work space size, and whether they are
    stable. A stable sort keeps items with the same key in the same
    relative order. The available algorithms have the following
    properties:

    ================= ======= ============= ============ =======
       kind            speed   worst case    work space  stable
    ================= ======= ============= ============ =======
    'introselect'        1        O(n)           0         no
    ================= ======= ============= ============ =======

    All the partition algorithms make temporary copies of the data when
    partitioning along any but the last axis.  Consequently,
    partitioning along the last axis is faster and uses less space than
    partitioning along any other axis.

    The sort order for complex numbers is lexicographic. If both the
    real and imaginary parts are non-nan then the order is determined by
    the real parts except when they are equal, in which case the order
    is determined by the imaginary parts.

    Examples
    --------
    >>> a = np.array([3, 4, 2, 1])
    >>> np.partition(a, 3)
    array([2, 1, 3, 4])

    >>> np.partition(a, (1, 3))
    array([1, 2, 3, 4])