# Getting Started¶

## Create¶

To start, lets construct a sparse `COO`

array from a `numpy.ndarray`

:

```
import numpy as np
import sparse
x = np.random.random((100, 100, 100))
x[x < 0.9] = 0 # fill most of the array with zeros
s = sparse.COO(x) # convert to sparse array
```

These store the same information and support many of the same operations, but the sparse version takes up less space in memory

```
>>> x.nbytes
8000000
>>> s.nbytes
1102706
>>> s
<COO: shape=(100, 100, 100), dtype=float64, nnz=100246>
```

For more efficient ways to construct sparse arrays, see documentation on Constructing Arrays.

## Compute¶

Many of the normal Numpy operations work on `COO`

objects just like on `numpy.ndarray`

objects.
This includes arithmetic, numpy.ufunc operations, or functions like tensordot and transpose.

```
>>> np.sin(s) + s.T * 1
<COO: shape=(100, 100, 100), dtype=float64, nnz=189601>
```

However, operations which convert the sparse array into a dense one will raise exceptions
For example, the following raises a `ValueError`

.

```
>>> y = x + 5
ValueError: Performing this operation would produce a dense result: <built-in function add>
```

However, if you’re sure you want to convert a sparse array to a dense one,
you can use the `todense`

method (which will result in a `numpy.ndarray`

):

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

For more operations see the Operations documentation or the API reference.