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

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

Attributes

shape
ndim	int(x[, base]) -> integer
nnz

dtype

dtype

Data type of the matrix

Methods

asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
clear	D.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)
fromkeys(...)	v defaults to None.
get(key[, default])	This overrides the dict.get method, providing type checking
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse
getformat()
getmaxprint()
getnnz()
getrow(i)	Returns a copy of row i of the matrix, as a (1 x n) sparse
has_key	D.has_key(k) -> True if D has a key k, else False
items	D.items() -> list of D’s (key, value) pairs, as 2-tuples
iteritems	D.iteritems() -> an iterator over the (key, value) items of D
iterkeys	D.iterkeys() -> an iterator over the keys of D
itervalues	D.itervalues() -> an iterator over the values of D
keys	D.keys() -> list of D’s keys
mean([axis])	Average the matrix over the given axis.
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
pop	D.pop(k[,d]) -> v, remove specified key and return the corresponding value.
popitem	D.popitem() -> (k, v), remove and return some (key, value) pair as a
reshape(shape)
resize(shape)	Resize the matrix in-place to dimensions given by ‘shape’.
set_shape(shape)
setdefault	D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D
setdiag(values[, k])	Fills the diagonal elements {a_ii} with the values from the given sequence.
split(cols_or_rows[, columns])
sum([axis])	Sum the matrix over the given axis.
take(cols_or_rows[, columns])
toarray()
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()
todia()
todok([copy])
tolil()
transpose()	Return the transpose
update	D.update(E, **F) -> None. Update D from dict/iterable E and F.
values	D.values() -> list of D’s values
viewitems	D.viewitems() -> a set-like object providing a view on D’s items
viewkeys	D.viewkeys() -> a set-like object providing a view on D’s keys
viewvalues	D.viewvalues() -> an object providing a view on D’s values