dok_array#
- 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’
- Attributes:
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.
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
()__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