scipy.sparse.

# dok_array#

class scipy.sparse.dok_array(arg1, shape=None, dtype=None, copy=False)[source]#

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:
dtypedtype

Data type of the array

shape2-tuple

Shape of the array

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

size

Number of stored values.

T

Transpose.

Methods

 __getitem__(key) x.__getitem__(y) <==> x[y] __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() conj([copy]) Element-wise complex conjugation. DEPRECATED: Return the conjugate transpose. conjugate([copy]) Element-wise complex conjugation. copy() Returns a copy of this array/matrix. 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() 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) Point-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/matrix. 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: 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()
 __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