# Slicing¶

Dask array supports most of the NumPy slicing syntax. In particular it supports the following:

• Slicing by integers and slices x[0, :5]
• Slicing by lists/arrays of integers x[[1, 2, 4]]
• Slicing by lists/arrays of booleans x[[False, True, True, False, True]]
• Slicing one ~dask.array.Array with a ~dask.array.Array of bools x[x > 0]
• Slicing one ~dask.array.Array with a zero or one-dimensional ~dask.array.Array of ints a[b.argtopk(5)]

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 vindex.

## Efficiency¶

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.