# Getting Started¶

`COO`

arrays can be constructed from `numpy.ndarray`

objects and
`scipy.sparse.spmatrix`

objects. For example, to generate the identity
matrix,

```
import numpy as np
import scipy.sparse
import sparse
sps_identity = scipy.sparse.eye(5)
identity = sparse.COO.from_scipy_sparse(sps_identity)
```

`COO`

arrays can have operations performed on them just like `numpy.ndarray`

objects. For example, to add two `COO`

arrays:

```
z = x + y
```

You can also apply any `numpy.ufunc`

to `COO`

arrays.

```
sin_x = np.sin(x)
```

However, operations which convert the sparse array into a dense one aren’t currently
supported. For example, the following raises a `ValueError`

.

```
y = x + 5
```

However, if you’re sure you want to convert a sparse array to a dense one, you can
do this (which will result in a `numpy.ndarray`

):

```
y = x.todense() + 5
```

That’s it! You can move on to the user manual to see what part of this library interests you, or you can jump straight in with the API reference.