numpy.partition(a, kth, axis=-1, kind='introselect', order=None)
[source]
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.
New in version 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 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 |
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Returns: |
partitioned_array : ndarray Array of the same type and shape as |
See also
ndarray.partition
argpartition
sort
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.
>>> 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])
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.partition.html