- dask.array.topk(a, k, axis=- 1, split_every=None)[source]¶
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.
This performs best when
kis much smaller than the chunk size. All results will be returned in a single chunk along the given axis.
- x: Array
Data being sorted
- k: int
- axis: int, optional
- split_every: int >=2, optional
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.
- Selection of x with size abs(k) along the given axis.
>>> 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]) >>> d.topk(-2).compute() array([1, 3])