numpy.argpartition

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

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:

aarray_like

Array to sort.

kthint 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.

axisint 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’

orderstr 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_arrayndarray, 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

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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]])

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