topk(a, k, axis=-1, split_every=None)

This performs best when k is much smaller than the chunk size. All results will be returned in a single chunk along the given axis.

Parameters

x: Array :

Data being sorted

k: int :

axis: int, optional :

split_every: int >=2, optional :

See reduce . This parameter becomes very important when k is on the same order of magnitude of the chunk size or more, as it prevents getting the whole or a significant portion of the input array in memory all at once, with a negative impact on network transfer too when running on distributed.

Returns

Selection of x with size abs(k) along the given axis.

Extract the k largest elements from a on the given axis, and return them sorted from largest to smallest. If k is negative, extract the -k smallest elements instead, and return them sorted from smallest to largest.

Examples

This example is valid syntax, but we were not able to check execution

>>> import dask.array as da
... x = np.array([5, 1, 3, 6])
... d = da.from_array(x, chunks=2)
... d.topk(2).compute()
array([6, 5])

This example is valid syntax, but we were not able to check execution

>>> d.topk(-2).compute()
array([1, 3])

See :

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SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

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File: /dask/array/reductions.py#1464
type: <class 'function'>
Commit: