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# 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

  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) |
  `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`((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) |
  `mean`([axis]) | Average the matrix over the given axis.
  `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]) | 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`([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 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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