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coo_array — SciPy v1.16.1 Manual

scipy.sparse.

class scipy.sparse.coo_array(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)[source]#

A sparse array in COOrdinate format.

Also known as the ‘ijv’ or ‘triplet’ format.

This can be instantiated in several ways:

coo_array(D)

where D is an ndarray

coo_array(S)

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

coo_array(shape, [dtype])

to construct an empty sparse array with shape shape dtype is optional, defaulting to dtype=’d’.

coo_array((data, coords), [shape])

to construct from existing data and index arrays:

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

2. coords[i][:] the axis-i coordinates of the data entries

Where A[coords] = data, and coords is a tuple of index arrays. When shape is not specified, it is inferred from the index arrays.

Attributes:

dtypedtype

Data type of the sparse array

shapetuple of integers

Shape of the sparse array

ndimint

Number of dimensions of the sparse array

nnz

Number of stored values, including explicit zeros.

size

Number of stored values.

data

COO format data array of the sparse array

coords

COO format tuple of index arrays

has_canonical_formatbool

Whether the matrix has sorted coordinates and no duplicates

format

Format string for matrix.

T

Transpose.

Methods

Notes

Sparse arrays 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 arrays

• Once a COO array 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

>>> # Constructing an empty sparse array
>>> import numpy as np
>>> from scipy.sparse import coo_array
>>> coo_array((3, 4), dtype=np.int8).toarray()
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int8)

>>> # Constructing a sparse array 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_array((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 sparse array with duplicate coordinates
>>> 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_array((data, (row, col)), shape=(4, 4))
>>> # Duplicate coordinates 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]])

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