SciPy

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

>>> 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

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

Methods

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
getH()
get_shape()
getcol(j) Returns a copy of column j of the matrix as a (m x 1)
getformat()
getmaxprint()
getnnz()
getrow(i) Returns a copy of row i of the matrix as a (1 x n)
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.
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]) See the docstring for spmatrix.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([order, out]) Return a dense matrix representation of this matrix.
todia()
todok([copy])
tolil()
transpose() Return the transpose
update((E, ...) If E has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)