bundles / scipy 1.17.1 / scipy / sparse / _lil / lil_array
ABCMeta
scipy.sparse._lil:lil_array
source: /scipy/sparse/_lil.py :498
Signature
def lil_array ( arg1 , shape = None , dtype = None , copy = False , * , maxprint = None ) Summary
Row-based LIst of Lists sparse array.
Extended Summary
This is a structure for constructing sparse arrays incrementally. Note that inserting a single item can take linear time in the worst case; to construct the array efficiently, make sure the items are pre-sorted by index, per row.
This can be instantiated in several ways:
lil_array(D)
where D is a 2-D ndarray
lil_array(S)
with another sparse array or matrix S (equivalent to S.tolil())
lil_array((M, N), [dtype])
to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'.
Attributes
dtype: dtypeData type of the array
shape: 2-tupleShape of the array
ndim: intNumber of dimensions (this is always 2)
nnzsizedataLIL format data array of the array
rowsLIL format row index array of the array
T
Notes
Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.
Advantages of the LIL format
supports flexible slicing
changes to the array sparsity structure are efficient
Disadvantages of the LIL format
arithmetic operations LIL + LIL are slow (consider CSR or CSC)
slow column slicing (consider CSC)
slow matrix vector products (consider CSR or CSC)
Intended Usage
LIL is a convenient format for constructing sparse arrays
once an array has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
consider using the COO format when constructing large arrays
Data Structure
An array (
self.rows) of rows, each of which is a sorted list of column indices of non-zero elements.The corresponding nonzero values are stored in similar fashion in
self.data.
Aliases
-
scipy.sparse.lil_array