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scipy.sparse.

# dok_array

class scipy.sparse.dok_array(arg1, shape=None, dtype=None, copy=False, *, maxprint=None)

  Dictionary Of Keys based sparse array.

  This is an efficient structure for constructing sparse arrays incrementally.

  This can be instantiated in several ways:
    dok_array(D)

      where D is a 2-D ndarray

    dok_array(S)

      with another sparse array or matrix S (equivalent to S.todok())

    dok_array((M,N), [dtype])

      create the array with initial shape (M,N) dtype is optional, defaulting to dtype=’d’

  Attributes:
    dtype dtype

      Data type of the array

    shape 2-tuple

      Shape of the array

    ndim int

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

    `size`

      Number of stored values.

    `T`

      Transpose.

  Methods

  `__len__`() | Return len(self).
  `asformat`(format[, copy]) | Return this array/matrix in the passed format.
  `astype`(dtype[, casting, copy]) | Cast the array/matrix elements to a specified type.
  `clear`() | Remove all items from the dict.
  `conj`([copy]) | Element-wise complex conjugation.
  `conjugate`([copy]) | Element-wise complex conjugation.
  `copy`() | Returns a copy of this array/matrix.
  `count_nonzero`([axis]) | Number of non-zero entries, equivalent to
  `diagonal`([k]) | Returns the kth diagonal of the array/matrix.
  `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 provides dict.get method functionality with type checking
  `items`() | Return a set-like object providing a view on the dict's items.
  `keys`() | Return a set-like object providing a view on the dict's keys.
  `maximum`(other) | Element-wise maximum between this and another array/matrix.
  `mean`([axis, dtype, out]) | Compute the arithmetic mean along the specified axis.
  `minimum`(other) | Element-wise minimum between this and another array/matrix.
  `multiply`(other) | Element-wise multiplication by another array/matrix.
  `nonzero`() | Nonzero indices of the array/matrix.
  `pop`(k[,d]) | If the key is not found, return the default if given; otherwise, raise a KeyError.
  `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/matrix without changing its data.
  `resize`(*shape) | Resize the array/matrix in-place to dimensions given by `shape`
  `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/matrix.
  `sum`([axis, dtype, out]) | Sum the array/matrix elements over a given axis.
  `toarray`([order, out]) | Return a dense ndarray representation of this sparse array/matrix.
  `tobsr`([blocksize, copy]) | Convert this array/matrix to Block Sparse Row format.
  `tocoo`([copy]) | Convert this array/matrix to COOrdinate format.
  `tocsc`([copy]) | Convert this array/matrix to Compressed Sparse Column format.
  `tocsr`([copy]) | Convert this array/matrix to Compressed Sparse Row format.
  `todense`([order, out]) | Return a dense representation of this sparse array.
  `todia`([copy]) | Convert this array/matrix to sparse DIAgonal format.
  `todok`([copy]) | Convert this array/matrix to Dictionary Of Keys format.
  `tolil`([copy]) | Convert this array/matrix to List of Lists format.
  `trace`([offset]) | Returns the sum along diagonals of the sparse array/matrix.
  `transpose`([axes, copy]) | Reverses the dimensions of the sparse array/matrix.
  `update`([E, ]**F) | If E is present and has a .keys() method, then does: for k in E.keys(): 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`() | Return an object providing a view on the dict's values.

  __getitem__ |
  __mul__ |

  Notes

  Sparse arrays 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_array 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