Dask array supports most of the NumPy slicing syntax. In particular it supports the following:
- Slicing by integers and slices
- Slicing by lists/arrays of integers
x[[1, 2, 4]]
- Slicing by lists/arrays of booleans
x[[False, True, True, False, True]]
- Slicing one
x[x > 0]
It does not currently support the following:
- Slicing with lists in multiple axes
x[[1, 2, 3], [3, 2, 1]]
This is straightforward to add though. If you have a use case then raise an
issue. Also users interested in this should take a look at
The normal dask schedulers are smart enough to compute only those blocks that are necessary to achieve the desired slicing. So large operations may be cheap if only a small output is desired.
In the example below we create a trillion element Dask array in million element blocks. We then operate on the entire array and finally slice out only a portion of the output.
>>> Trillion element array of ones, in 1000 by 1000 blocks >>> x = da.ones((1000000, 1000000), chunks=(1000, 1000)) >>> da.exp(x)[:1500, :1500] ...
This only needs to compute the top-left four blocks to achieve the result. We are still slightly wasteful on those blocks where we need only partial results. We are also a bit wasteful in that we still need to manipulate the dask-graph with a million or so tasks in it. This can cause an interactive overhead of a second or two.
But generally, slicing works well.