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: 

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 : str 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_array : ndarray, 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. 
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])