# Assignment¶

Dask Array supports most of the NumPy assignment indexing syntax. In particular, it supports combinations of the following:

• Indexing by integers: `x = y`

• Indexing by slices: `x[2::-1] = y`

• Indexing by a list of integers: `x[[0, -1, 1]] = y`

• Indexing by a 1-d `numpy` array of integers: `x[np.arange(3)] = y`

• Indexing by a 1-d `Array` of integers: `x[da.arange(3)] = y`, `x[da.from_array([0, -1, 1])] = y`, `x[da.where(np.array([1, 2, 3]) < 3)] = y`

• Indexing by a list of booleans: `x[[False, True, True]] = y`

• Indexing by a 1-d `numpy` array of booleans: `x[np.arange(3) > 0] = y`

It also supports:

However, it does not currently support the following:

• Indexing with lists in multiple axes: `x[[1, 2, 3], [3, 1, 2]] = y`

The normal NumPy broadcasting rules apply:

```>>> x = da.zeros((2, 6))
>>> x = 1
>>> x[..., 1] = 2.0
>>> x[:, 2] = [3, 4]
>>> x[:, 5:2:-2] = [[6, 5]]
>>> x.compute()
array([[1., 2., 3., 5., 1., 6.],
[0., 2., 4., 5., 0., 6.]])
>>> x = -x
>>> x.compute()
array([[ 1.,  2.,  3.,  5.,  1.,  6.],
[-1., -2., -3., -5., -1., -6.]])
```

Elements may be masked by assigning to the NumPy masked value, or to an array with masked values:

```>>> x = da.ones((2, 6))
>>> x[0, [1, -2]] = np.ma.masked
>>> x = np.ma.array([0, 1, 2, 3, 4, 5], mask=[0, 1, 1, 0, 0, 0])
>>> print(x.compute())
[[1.0 -- 1.0 1.0 -- 1.0]
[0.0 -- -- 3.0 4.0 5.0]]
>>> x[:, 0] = x[:, 1]
>>> print(x.compute())
[[1.0 -- 1.0 1.0 -- 1.0]
[0.0 -- -- 3.0 4.0 5.0]]
>>> x[:, 0] = x[:, 1]
>>> print(x.compute())
[[-- -- 1.0 1.0 -- 1.0]
[-- -- -- 3.0 4.0 5.0]]
```

If, and only if, a single broadcastable `Array` of booleans is provided then masked array assignment does not yet work as expected. In this case the data underlying the mask are assigned:

```>>> x = da.arange(12).reshape(2, 6)
>>> x[x > 7] = np.ma.array(-99, mask=True)
>>> print(x.compute())
[[  0   1   2   3   4   5]
[  6   7 -99 -99 -99 -99]]
```

Note that masked assignments do work when a boolean `Array` index used in a tuple, or implicit tuple, of indices:

```>>> x = da.arange(12).reshape(2, 6)
>>> x[1, x > 3] = np.ma.masked
>>> print(x.compute())
[[0 1 2 3 4 5]
[6 7 8 9 -- --]]
>>> x = da.arange(12).reshape(2, 6)
>>> print(x.compute())
[[ 0  1  2  3  4  5]
[ 6  7  8  9 10 11]]
>>> x[(x[:, 2] < 4,)] = np.ma.masked
>>> print(x.compute())
[[-- -- -- -- -- --]
[6 7 8 9 10 11]]
```