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)
with a dense array, D
- dok_array(S)
with a sparse array, S
- 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 array 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
Methods
__len__()Return len(self).
__mul__(*args, **kwargs)asformat(format[, copy])Return this matrix in the passed format.
asfptype()Upcast matrix to a floating point format (if necessary)
astype(dtype[, casting, copy])Cast the matrix elements to a specified type.
clear()conj([copy])Element-wise complex conjugation.
Return the conjugate transpose.
conjugate([copy])Element-wise complex conjugation.
copy()Returns a copy of this matrix.
Number of non-zero entries, equivalent to
diagonal([k])Returns the kth diagonal of the 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 overrides the dict.get method, providing type checking but otherwise equivalent functionality.
getH()Return the Hermitian transpose of this matrix.
Get shape of a matrix.
getcol(j)Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).
Format of a matrix representation as a string.
Maximum number of elements to display when printed.
getnnz([axis])Number of stored values, including explicit zeros.
getrow(i)Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector).
items()keys()maximum(other)Element-wise maximum between this and another matrix.
mean([axis, dtype, out])Compute the arithmetic mean along the specified axis.
minimum(other)Element-wise minimum between this and another matrix.
multiply(other)Point-wise multiplication by another matrix
nonzero()nonzero indices
pop(key[, default])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 matrix without changing its data.
resize(*shape)Resize the matrix in-place to dimensions given by
shapeset_shape(shape)See
reshape.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 matrix elements over a given axis.
toarray([order, out])Return a dense ndarray representation of this matrix.
tobsr([blocksize, copy])Convert this matrix to Block Sparse Row format.
tocoo([copy])Convert this matrix to COOrdinate format.
tocsc([copy])Convert this matrix to Compressed Sparse Column format.
tocsr([copy])Convert this matrix to Compressed Sparse Row format.
todense([order, out])Return a dense matrix representation of this matrix.
todia([copy])Convert this matrix to sparse DIAgonal format.
todok([copy])Convert this matrix to Dictionary Of Keys format.
tolil([copy])Convert this matrix to List of Lists format.
trace([offset])Returns the sum along diagonals of the sparse matrix.
transpose([axes, copy])Reverses the dimensions of the sparse 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__