# 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

 shape Get shape of a matrix. nnz Number of stored values, including explicit zeros.
 dtype (dtype) Data type of the matrix ndim (int) Number of dimensions (this is always 2)

Methods

 asformat(format) Return this matrix in a given sparse format asfptype() Upcast matrix to a floating point format (if necessary) astype(t) Cast the matrix elements to a specified type. clear(() -> None.  Remove all items from D.) conj() Element-wise complex conjugation. conjtransp() Return the conjugate transpose conjugate() Element-wise complex conjugation. copy() Returns a copy of this matrix. count_nonzero() Number of non-zero entries, equivalent to 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() 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 a (m x 1) DOK matrix. 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) 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) 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]) -> 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]) Element-wise power. reshape(shape[, order]) 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]) -> D.get(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, ...) 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(...)

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