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_array(D)

with a dense matrix, D

dok_array(S)

with a sparse matrix, S

dok_array((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_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

Attributes:

dtypedtype

Data type of the matrix

shape2-tuple

Get shape of a sparse array.

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

Methods

__len__()	Return len(self).
__mul__(other)
asformat(format[, copy])	Return this array in the passed format.
asfptype()	Upcast array to a floating point format (if necessary)
astype(dtype[, casting, copy])	Cast the array 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 array.
count_nonzero()	Number of non-zero entries, equivalent to
diagonal([k])	Returns the kth diagonal of the array.
dot(other)	Ordinary dot product
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set 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 array.
get_shape()	Get shape of a sparse array.
getcol(j)	Returns a copy of column j of the array, as an (m x 1) sparse array (column vector).
getformat()	Matrix storage format
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 array, as a (1 x n) sparse array (row vector).
items()
keys()
maximum(other)	Element-wise maximum between this and another array.
mean([axis, dtype, out])	Compute the arithmetic mean along the specified axis.
minimum(other)	Element-wise minimum between this and another array.
multiply(other)	Point-wise multiplication by another array
nonzero()	nonzero indices
pop(key[, default])	If key is not found, default is returned if given, otherwise KeyError is raised
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
power(n[, dtype])	Element-wise power.
reshape(self, shape[, order, copy])	Gives a new shape to a sparse array without changing its data.
resize(*shape)	Resize the array in-place to dimensions given by shape
set_shape(shape)	Set the shape of the matrix in-place
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
setdiag(values[, k])	Set diagonal or off-diagonal elements of the array.
sum([axis, dtype, out])	Sum the array elements over a given axis.
toarray([order, out])	Return a dense ndarray representation of this sparse array.
tobsr([blocksize, copy])	Convert this array to Block Sparse Row format.
tocoo([copy])	Convert this array to COOrdinate format.
tocsc([copy])	Convert this array to Compressed Sparse Column format.
tocsr([copy])	Convert this array to Compressed Sparse Row format.
todense([order, out])	Return a dense matrix representation of this sparse array.
todia([copy])	Convert this array to sparse DIAgonal format.
todok([copy])	Convert this array to Dictionary Of Keys format.
tolil([copy])	Convert this array to List of Lists format.
trace([offset])	Returns the sum along diagonals of the sparse array.
transpose([axes, copy])	Reverses the dimensions of the sparse array.
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__