`scipy.sparse._dok:dok_array`

source: build-install/usr/lib/python3.14/site-packages/scipy/sparse/_dok.py:687

Signature

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

Summary

Dictionary Of Keys based sparse array.

Extended Summary

This is an efficient structure for constructing sparse arrays incrementally.

This can be instantiated in several ways:

dok_array(D): where D is a 2-D ndarray
dok_array(S): with another sparse array or matrix S (equivalent to S.todok())
dok_array((M,N), [dtype]): create the array with initial shape (M,N) dtype is optional, defaulting to dtype='d'

Attributes

dtype : dtype: Data type of the array
shape : 2-tuple: Shape of the array
ndim : int: Number of dimensions (this is always 2)
format : str: Three letter code for the format of the array storage, e.g. 'dok'
nnz : int: Number of values stored in the array
size : int: Number of values stored in the array
T : dok_array: The transpose of the array
mT : dok_array: The matrix transpose of the array

Notes

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

Allows for efficient O(1) access of individual elements.
Duplicates are not allowed.
Can be efficiently converted to a coo_array once constructed.

Examples

import numpy as np
from scipy.sparse import dok_array
S = dok_array((5, 5), dtype=np.float32)
for i in range(5):
    for j in range(5):
        S[i, j] = i + j    # Update element