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, 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.