# 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_array(D)

      with a dense matrix, D

    dok_array(S)

      with a sparse matrix, S

    dok_array((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_array
      >>> S = dok_array((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

      Get shape of a sparse array.

    ndim int

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

  Methods

  `__len__`() | Return len(self).
  `__mul__`(other) |
  `asformat`(format[, copy]) | Return this array in the passed format.
  `asfptype`() | Upcast array to a floating point format (if necessary)
  `astype`(dtype[, casting, copy]) | Cast the array elements to a specified type.
  `clear`() |
  `conj`([copy]) | Element-wise complex conjugation.
  `conjtransp`() | Return the conjugate transpose.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array.
  `count_nonzero`() | Number of non-zero entries, equivalent to
  `diagonal`([k]) | Returns the kth diagonal of the array.
  `dot`(other) | Ordinary dot product
  `fromkeys`(iterable[, value]) | Create a new dictionary with keys from iterable and values set to value.
  `get`(key[, default]) | This overrides the dict.get method, providing type checking but otherwise equivalent functionality.
  `getH`() | Return the Hermitian transpose of this array.
  `get_shape`() | Get shape of a sparse array.
  `getcol`(j) | Returns a copy of column j of the array, as an (m x 1) sparse array (column vector).
  `getformat`() | Matrix storage format
  `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 array, as a (1 x n) sparse array (row vector).
  `items`() |
  `keys`() |
  `maximum`(other) | Element-wise maximum between this and another array.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `minimum`(other) | Element-wise minimum between this and another array.
  `multiply`(other) | Point-wise multiplication by another array
  `nonzero`() | nonzero indices
  `pop`(key[, default]) | If key is not found, default is returned if given, otherwise KeyError is raised
  `popitem`(/) | Remove and return a (key, value) pair as a 2-tuple.
  `power`(n[, dtype]) | Element-wise power.
  `reshape`(self, shape[, order, copy]) | Gives a new shape to a sparse array without changing its data.
  `resize`(*shape) | Resize the array in-place to dimensions given by `shape`
  `set_shape`(shape) | Set the shape of the matrix in-place
  `setdefault`(key[, default]) | Insert key with a value of default if key is not in the dictionary.
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array.
  `sum`([axis, dtype, out]) | Sum the array elements over a given axis.
  `toarray`([order, out]) | Return a dense ndarray representation of this sparse array.
  `tobsr`([blocksize, copy]) | Convert this array to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array to COOrdinate format.
  `tocsc`([copy]) | Convert this array to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this array to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense matrix representation of this sparse array.
  `todia`([copy]) | Convert this array to sparse DIAgonal format.
  `todok`([copy]) | Convert this array to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array.
  `update`([E, ]**F) | If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
  `values`() |

  __getitem__ |