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)- Element-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