5.2.1.1.6. scipy.sparse.dok_matrix¶

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:

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

dtype	(dtype) Data type of the matrix
shape	(2-tuple) Shape of the matrix
ndim	(int) Number of dimensions (this is always 2)
nnz	Number of nonzero elements

__init__(arg1, shape=None, dtype=None, copy=False)[source]¶

Methods

__init__(arg1[, shape, dtype, copy])
asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
clear(() -> None.  Remove all items from D.)
conj()
conjtransp()	Return the conjugate transpose
conjugate()
copy()
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.
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix as a (m x 1) DOK matrix.
getformat()
getmaxprint()
getnnz()
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)
itervalues(...)
keys(() -> list of D’s keys)
maximum(other)
mean([axis])	Average the matrix over the given axis.
minimum(other)
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)
resize(shape)	Resize the matrix in-place to dimensions given by ‘shape’.
set_shape(shape)
setdefault((k[,d]) -> D.get(k,d), ...)
setdiag(values[, k])	Set diagonal or off-diagonal elements of the array.
sum([axis])	Sum the matrix over the given axis.
toarray([order, out])	Return a dense ndarray representation of this matrix.
tobsr([blocksize])
tocoo()	Return a copy of this matrix in COOrdinate format
tocsc()	Return a copy of this matrix in Compressed Sparse Column format
tocsr()	Return a copy of this matrix in Compressed Sparse Row format
todense([order, out])	Return a dense matrix representation of this matrix.
todia()
todok([copy])
tolil()
transpose()	Return the transpose
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)
viewitems(...)
viewkeys(...)
viewvalues(...)

Attributes

ndim
nnz
shape