scipy.sparse.dok_matrix

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

Dictionary Of Keys based sparse matrix.

This is an efficient structure for constructing sparse matrices incrementally.

This can be instantiated in several ways:

dok_matrix(D)

with a dense matrix, D

dok_matrix(S)

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’

Notes

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.

Examples

>>> 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

Attributes

dtypedtype

Data type of the matrix

shape2-tuple

Get shape of a matrix.

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

Methods

__len__()	Return len(self).
__mul__(other)	interpret other and call one of the following
asformat(format[, copy])	Return this matrix in the passed format.
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(dtype[, casting, copy])	Cast the matrix elements to a specified type.
clear()
conj([copy])	Element-wise complex conjugation.
conjtransp()	Return the conjugate transpose.
conjugate([copy])	Element-wise complex conjugation.
copy()	Returns a copy of this matrix.
count_nonzero()	Number of non-zero entries, equivalent to
diagonal([k])	Returns the k-th diagonal of the matrix.
dot(other)	Ordinary dot product
fromkeys($type, iterable[, value])	Returns a new dict with keys from iterable and values equal to value.
get(key[, default])	This overrides the dict.get method, providing type checking but otherwise equivalent functionality.
getH()	Return the Hermitian transpose of this matrix.
get_shape()	Get shape of a matrix.
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).
getformat()	Format of a matrix representation as a string.
getmaxprint()	Maximum number of elements to display when printed.
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) sparse matrix (row vector).
items()
keys()
maximum(other)	Element-wise maximum between this and another matrix.
mean([axis, dtype, out])	Compute the arithmetic mean along the specified axis.
minimum(other)	Element-wise minimum between this and another matrix.
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
power(n[, dtype])	Element-wise power.
reshape(self, shape[, order, copy])	Gives a new shape to a sparse matrix without changing its data.
resize(*shape)	Resize the matrix in-place to dimensions given by shape
set_shape(shape)	See reshape.
setdefault(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, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

__getitem__