Attributes

dtype : dtype

Data type of the array

shape : 2-tuple

Shape of the array

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

COO format data array of the array

row :

COO format row index array of the array

col :

COO format column index array of the array

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

This can be instantiated in several ways:

coo_array(D)

with a dense array D

coo_array(S)

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

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

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

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

to construct from three arrays:

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

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

3. j[:] the column indices of the array entries

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

Notes

Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and array 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 array has been constructed, convert to CSR or CSC format for fast arithmetic and array 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 arrays and the like. (see example)

A sparse array in COOrdinate format.

Examples

>>> # Constructing an empty array
... 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 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 array 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_array((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._arrays.coo_array networkx.convert_matrix.to_scipy_sparse_array

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