dok_array — SciPy v1.15.2 Manual (original) (raw)

scipy.sparse.

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

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

__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([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()
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.
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()