class scipy.sparse.dok_matrix(arg1, shape=None, dtype=None, copy=False)[source]

Dictionary Of Keys based sparse matrix.

This is an efficient structure for constructing sparse matrices incrementally.

This can be instantiated in several ways:
with a dense matrix, D
with a sparse matrix, S
dok_matrix((M,N), [dtype])
create the matrix with initial shape (M,N) dtype is optional, defaulting to dtype=’d’


Sparse matrices 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_matrix once constructed.


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


nnz Number of stored values, including explicit zeros.
dtype (dtype) Data type of the matrix
shape (2-tuple) Shape of the matrix
ndim (int) Number of dimensions (this is always 2)


asformat(format) Return this matrix in a given sparse format
asfptype() Upcast matrix to a floating point format (if necessary)
clear(() -> None.  Remove all items from D.)
conjtransp() Return the conjugate transpose
copy() Returns a copy of this matrix.
count_nonzero() Number of non-zero entries, equivalent to
diagonal() Returns the main diagonal of the matrix
dot(other) Ordinary dot product
fromkeys(...) v defaults to None.
get(key[, default]) This overrides the dict.get method, providing type checking but otherwise equivalent functionality.
getcol(j) Returns a copy of column j of the matrix as a (m x 1) DOK matrix.
getnnz([axis]) Number of stored values, including explicit zeros.
getrow(i) Returns a copy of row i of the matrix as a (1 x n) DOK matrix.
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
keys(() -> list of D’s keys)
mean([axis, dtype, out]) Compute the arithmetic mean along the specified axis.
multiply(other) Point-wise multiplication by another matrix
nonzero() nonzero indices
pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty.
power(n[, dtype])
reshape(shape[, order]) Gives a new shape to a sparse matrix without changing its data.
resize(shape) Resize the matrix in-place to dimensions given by ‘shape’.
setdefault((k[,d]) -> D.get(k,d), ...)
setdiag(values[, k]) Set diagonal or off-diagonal elements of the array.
sum([axis, dtype, out]) Sum the matrix elements over a given axis.
toarray([order, out]) Return a dense ndarray representation of this matrix.
tobsr([blocksize, copy]) Convert this matrix to Block Sparse Row format.
tocoo([copy]) Convert this matrix to COOrdinate format.
tocsc([copy]) Convert this matrix to Compressed Sparse Column format.
tocsr([copy]) Convert this matrix to Compressed Sparse Row format.
todense([order, out]) Return a dense matrix representation of this matrix.
todia([copy]) Convert this matrix to sparse DIAgonal format.
todok([copy]) Convert this matrix to Dictionary Of Keys format.
tolil([copy]) Convert this matrix to LInked List format.
transpose([axes, copy]) Reverses the dimensions of the sparse matrix.
update(([E, ...) If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)