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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 :

Number of stored values, including explicit zeros

data :

LIL format data array of the matrix

rows :

LIL format row index array of the matrix

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 a matrix efficiently, make sure the items are pre-sorted by index, per row.

This can be instantiated in several ways:

lil_matrix(D)

with a dense matrix or rank-2 ndarray D

lil_matrix(S)

with another sparse 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'.

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 .

Row-based list of lists sparse matrix

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse._lil.isspmatrix_lil

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GitHub : /scipy/sparse/_lil.py#19
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