# scipy.sparse.dok_array#

class scipy.sparse.dok_array(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_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:
dtypedtype

Data type of the matrix

`shape`2-tuple

The shape of the array.

ndimint

Number of dimensions (this is always 2)

`nnz`

Number of stored values, including explicit zeros.

Methods

 Return len(self). `asformat`(format[, copy]) Return this array in the passed format. Upcast array to a floating point format (if necessary) `astype`(dtype[, casting, copy]) Cast the array elements to a specified type. `conj`([copy]) Element-wise complex conjugation. Return the conjugate transpose. `conjugate`([copy]) Element-wise complex conjugation. Returns a copy of this array. 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. Return the Hermitian transpose of this array. Get shape of a sparse array. Returns a copy of column j of the array, as an (m x 1) sparse array (column vector). Matrix storage format. Maximum number of elements to display when printed. `getnnz`([axis]) Number of stored values, including explicit zeros. Returns a copy of row i of the array, as a (1 x n) sparse array (row vector). `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 indices `pop`(key[, default]) If key is not found, default is returned if given, otherwise KeyError is raised 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) `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]
 __getitem__ __mul__