`scipy.sparse._coo:coo_array`

source: /scipy/sparse/_coo.py :1655

Signature

   def     coo_array (    arg1    ,    shape    =  None   ,    dtype    =  None   ,    copy    =  False   ,  * ,     maxprint    =  None     )

Summary

A sparse array in COOrdinate format.

Extended Summary

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:

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

dtype : dtype: Data type of the sparse array
shape : tuple of integers: Shape of the sparse array
ndim : int: Number of dimensions of the sparse array
nnz
size
data: COO format data array of the sparse array
coords: COO format tuple of index arrays
has_canonical_format : bool: Whether the matrix has sorted coordinates and no duplicates
format
T

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

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

✓

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()

✓

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))

✓

np.max(coo.data)

✗

coo.toarray()