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.. currentmodule:: sparse
Getting Started
===============
Install
-------
If you haven't already, install the ``sparse`` library
.. code-block:: bash
pip install sparse
Create
------
To start, lets construct a sparse :obj:`COO` array from a :obj:`numpy.ndarray`:
.. code-block:: python
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
.. code-block:: python
>>> 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.
.. code-block:: python
>>> 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.
.. code-block:: python
>>> 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`):
.. code-block:: python
y = s.todense() + 5
For more operations see the :doc:`Operations documentation <operations>`
or the :doc:`API reference <generated/sparse>`.
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