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.
New in version 1.8.0.
 Parameters
 aarray_like
Array to sort.
 kthint or sequence of ints
Element index to partition by. The kth 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 kth 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 onedimensional,
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.
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])
Multidimensional 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]])