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]
- Slicing one
Arraywith a zero or one-dimensional
However, 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. Hence, large operations may be cheap if only a small output is desired.
In the example below, we create a Dask array with a trillion elements with million element sized 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 slightly wasteful on those blocks where we need only partial results. Moreover, 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.