`scipy.sparse._lil:lil_matrix`

source: /scipy/sparse/_lil.py :563

Signature

   def     lil_matrix (    arg1    ,    shape    =  None   ,    dtype    =  None   ,    copy    =  False   ,  * ,     maxprint    =  None     )

Summary

Row-based LIst of Lists sparse matrix.

Extended Summary

This is a structure for constructing sparse matrices incrementally. Note that inserting a single item can take linear time in the worst case; to construct the matrix efficiently, make sure the items are pre-sorted by index, per row.

This can be instantiated in several ways:

lil_matrix(D): where D is a 2-D ndarray
lil_matrix(S): with another sparse array or matrix S (equivalent to S.tolil())
lil_matrix((M, N), [dtype]): to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'.

Attributes

dtype : dtype: Data type of the matrix
shape : 2-tuple: Shape of the matrix
ndim : int: Number of dimensions (this is always 2)
nnz
size
data: LIL format data array of the matrix
rows: LIL format row index array of the matrix
T

Notes

Sparse matrices 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 matrix 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 matrices
once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations
consider using the COO format when constructing large matrices

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_matrix