# Getting Started¶

## Install¶

If you haven’t already, install the `sparse` library

```pip install sparse
```

## 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, fill_value=0.0>
```

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, fill_value=0.0>
```

However, operations which map zero elements to nonzero will usually change the fill-value instead of raising an error.

```>>> y = s + 5
<COO: shape=(100, 100, 100), dtype=float64, nnz=100246, fill_value=5.0>
```

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 = s.todense() + 5
```

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