# 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

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 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(...)

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