# 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: a : array_like Array to sort. kth : int 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. axis : int 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’ order : list, optional When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified. index_array : ndarray, int Array of indices that partition a along the specified axis. In other words, a[index_array] yields a sorted a.

partition
Describes partition algorithms used.
ndarray.partition
Inplace partition.
argsort
Full indirect sort

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])

numpy.partition

numpy.argmax