bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / sparse / _coo / coo_matrix

ABCMeta

`scipy.sparse._coo:coo_matrix`

source: build-install/usr/lib/python3.14/site-packages/scipy/sparse/_coo.py:1823

Signature

def coo_matrix(*args, **kwargs)

Members

Summary

A sparse matrix in COOrdinate format.

Extended Summary

Also known as the 'ijv' or 'triplet' format.

This can be instantiated in several ways:

coo_matrix(D)

where D is a 2-D ndarray

coo_matrix(S)

with another sparse array or matrix S (equivalent to S.tocoo())

coo_matrix((M, N), [dtype])

to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'.

coo_matrix((data, (i, j)), [shape=(M, N)])

to construct from three arrays:

data[:] the entries of the matrix, in any order
i[:] the row indices of the matrix entries
j[:] the column indices of the matrix entries

Where A[i[k], j[k]] = data[k]. When shape is not specified, it is inferred from the index arrays

Attributes

data : ndarray: COO format data array of the sparse matrix
coords : tuple of ndarray: COO format tuple of index matrix
has_canonical_format : bool: Whether the matrix has sorted coordinates and no duplicates
dtype : dtype: Data type of the matrix
shape : tuple of integers: Shape of the matrix
ndim : int: Number of dimensions of the matrix
format : str: Three letter code for the format of the matrix storage, e.g. 'coo'
nnz : int: Number of values stored in the matrix
size : int: Number of values stored in the matrix
T : coo_matrix: The transpose of the matrix
mT : coo_matrix: The matrix transpose

Notes

Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.

Advantages of the COO format

facilitates fast conversion among sparse formats
permits duplicate entries (see example)
very fast conversion to and from CSR/CSC formats

Disadvantages of the COO format

does not directly support:
- arithmetic operations
- slicing

Intended Usage

COO is a fast format for constructing sparse matrices
Once a COO matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example)

Canonical format

Entries and coordinates sorted by row, then column.
There are no duplicate entries (i.e. duplicate (i,j) locations)
Data arrays MAY have explicit zeros.

Examples

import numpy as np
from scipy.sparse import coo_matrix
coo_matrix((3, 4), dtype=np.int8).toarray()

✓

array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

row  = np.array([0, 3, 1, 0])
col  = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
coo_matrix((data, (row, col)), shape=(4, 4)).toarray()

✓

array([[4, 0, 9, 0],
       [0, 7, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 5]])

row  = np.array([0, 0, 1, 3, 1, 0, 0])
col  = np.array([0, 2, 1, 3, 1, 0, 0])
data = np.array([1, 1, 1, 1, 1, 1, 1])
coo = coo_matrix((data, (row, col)), shape=(4, 4))

✓

np.max(coo.data)

✗

np.int64(1)

coo.toarray()

✓

array([[3, 0, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 1]])

Aliases

scipy.sparse.coo_matrix