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Sparse arrays (scipy.sparse) — SciPy v1.16.0 Manual

SciPy 2-D sparse array package for numeric data.

Note

This package is switching to an array interface, compatible with NumPy arrays, from the older matrix interface. We recommend that you use the array objects (bsr_array, coo_array, etc.) for all new work.

When using the array interface, please note that:

• x * y no longer performs matrix multiplication, but element-wise multiplication (just like with NumPy arrays). To make code work with both arrays and matrices, use x @ y for matrix multiplication.

• Operations such as sum, that used to produce dense matrices, now produce arrays, whose multiplication behavior differs similarly.

• Sparse arrays use array style slicing operations, returning scalars, 1D, or 2D sparse arrays. If you need 2D results, use an appropriate index. E.g. A[:, i, None] or A[:, [i]].

• All index arrays for a given sparse array should be of same dtype. For example, for CSR format, indices and indptr should have the same dtype. For COO, each array in coords should have same dtype.

The construction utilities (eye, kron, random, diags, etc.) have appropriate replacements (see Building sparse arrays).

For more information see Migration from spmatrix to sparray.

Combining matrices use the same functions as for Combining arrays.

There are seven available sparse array types:

1. csc_array: Compressed Sparse Column format

2. csr_array: Compressed Sparse Row format

3. bsr_array: Block Sparse Row format

4. lil_array: List of Lists format

5. dok_array: Dictionary of Keys format

6. coo_array: COOrdinate format (aka IJV, triplet format)

7. dia_array: DIAgonal format

To construct an array efficiently, use any of coo_array, dok_array or lil_array. dok_array and lil_array support basic slicing and fancy indexing with a similar syntax to NumPy arrays. The COO format does not support indexing (yet) but can also be used to efficiently construct arrays using coord and value info.

Despite their similarity to NumPy arrays, it is strongly discouraged to use NumPy functions directly on these arrays because NumPy typically treats them as generic Python objects rather than arrays, leading to unexpected (and incorrect) results. If you do want to apply a NumPy function to these arrays, first check if SciPy has its own implementation for the given sparse array class, or convert the sparse array to a NumPy array (e.g., using the toarray method of the class) before applying the method.

All conversions among the CSR, CSC, and COO formats are efficient, linear-time operations.

To perform manipulations such as multiplication or inversion, first convert the array to either CSC or CSR format. The lil_array format is row-based, so conversion to CSR is efficient, whereas conversion to CSC is less so.

To do a vector product between a 2D sparse array and a vector use the matmul operator (i.e., @) which performs a dot product (like the dot method):

>>> import numpy as np
>>> from scipy.sparse import csr_array
>>> A = csr_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
>>> v = np.array([1, 0, -1])
>>> A @ v
array([ 1, -3, -1], dtype=int64)

The CSR format is especially suitable for fast matrix vector products.

Construct a 1000x1000 lil_array and add some values to it:

>>> from scipy.sparse import lil_array
>>> from scipy.sparse.linalg import spsolve
>>> from numpy.linalg import solve, norm
>>> from numpy.random import rand

>>> A = lil_array((1000, 1000))
>>> A[0, :100] = rand(100)
>>> A.setdiag(rand(1000))

Now convert it to CSR format and solve A x = b for x:

>>> A = A.tocsr()
>>> b = rand(1000)
>>> x = spsolve(A, b)

Convert it to a dense array and solve, and check that the result is the same:

>>> x_ = solve(A.toarray(), b)

Now we can compute norm of the error with:

>>> err = norm(x-x_)
>>> err < 1e-9
True

It should be small :)

Construct an array in COO format:

>>> from scipy import sparse
>>> from numpy import array
>>> I = array([0,3,1,0])
>>> J = array([0,3,1,2])
>>> V = array([4,5,7,9])
>>> A = sparse.coo_array((V,(I,J)),shape=(4,4))

Notice that the indices do not need to be sorted.

Duplicate (i,j) entries are summed when converting to CSR or CSC.

>>> I = array([0,0,1,3,1,0,0])
>>> J = array([0,2,1,3,1,0,0])
>>> V = array([1,1,1,1,1,1,1])
>>> B = sparse.coo_array((V,(I,J)),shape=(4,4)).tocsr()

This is useful for constructing finite-element stiffness and mass matrices.

CSR column indices are not necessarily sorted. Likewise for CSC row indices. Use the .sorted_indices() and .sort_indices() methods when sorted indices are required (e.g., when passing data to other libraries).

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