quickstart.rst

.. currentmodule:: sparse

Getting Started

Install

If you haven't already, install the sparse library

pip install sparse

Create

To start, lets construct a sparse :obj:`COO` array from a :obj:`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 :doc:`Constructing Arrays <construct>`.

Compute

Many of the normal Numpy operations work on :obj:`COO` objects just like on :obj:`numpy.ndarray` objects. This includes arithmetic, :doc:`numpy.ufunc <numpy:reference/ufuncs>` 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 :obj:`numpy.ndarray`):

y = s.todense() + 5

For more operations see the :doc:`Operations documentation <operations>` or the :doc:`API reference <generated/sparse>`.