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’

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
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.

conjtransp()

DEPRECATED: Return the conjugate transpose.

conjugate([copy])

Element-wise complex conjugation.

copy()

Returns a copy of this array/matrix.

count_nonzero()

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__
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