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.
Array to sort.
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.
Axis along which to sort. The default is -1 (the last axis). If
None, the flattened array is used.
Selection algorithm. Default is ‘introselect’
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.
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=a) always
yields the partitioned a, irrespective of dimensionality.
np.take_along_axis(a, index_array, axis=a)
Describes partition algorithms used.
Full indirect sort.
Apply index_array from argpartition to an array as if by calling partition.
See partition for notes on the different selection algorithms.
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])
>>> 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]])