Attributes

dtype : dtype

Data type of the matrix

shape : 2-tuple

Shape of the matrix

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

COO format data array of the matrix

row :

COO format row index array of the matrix

col :

COO format column index array of the matrix

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

This can be instantiated in several ways:

coo_matrix(D)

with a dense matrix D

coo_matrix(S)

with another sparse 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:

1. data[:] the entries of the matrix, in any order

2. i[:] the row indices of the matrix entries

3. 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

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 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)

A sparse matrix in COOrdinate format.

Examples

>>> # Constructing an empty matrix
... 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)

>>> # Constructing a matrix using ijv format
... 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]])

>>> # Constructing a matrix with duplicate indices
... 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))
... # Duplicate indices are maintained until implicitly or explicitly summed
... np.max(coo.data)
1

>>> coo.toarray()
array([[3, 0, 1, 0],
       [0, 2, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 1]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse._construct.bmat scipy.sparse._coo.isspmatrix_coo networkx.convert_matrix.from_scipy_sparse_array scipy.sparse._construct.block_diag scipy.sparse._construct.random scipy.sparse._construct.vstack scipy.sparse._construct.hstack networkx.convert_matrix.from_scipy_sparse_matrix scipy.sparse._coo.coo_matrix scipy.sparse._construct.rand scipy.sparse._coo.coo_matrix.tocsc scipy.sparse._coo.coo_matrix.tocsr

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

GitHub : /scipy/sparse/_coo.py#22
type: <class 'type'>
Commit: