scipy.sparse.lil_matrix

class scipy.sparse.lil_matrix(arg1, shape=None, dtype=None, copy=False)

Row-based linked list sparse matrix

This is an efficient structure for constructing sparse matrices incrementally.

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.

Attributes

shape
ndim	int(x[, base]) -> integer
nnz

dtype	dtype	Data type of the matrix
data		LIL format data array of the matrix
rows		LIL format row index array of the matrix

Methods

asformat(format)	Return this matrix in a given sparse format
asfptype()	Upcast matrix to a floating point format (if necessary)
astype(t)
conj()
conjugate()
copy()
diagonal()	Returns the main diagonal of the matrix
dot(other)
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse
getformat()
getmaxprint()
getnnz()
getrow(i)	Returns a copy of the ‘i’th row.
getrowview(i)	Returns a view of the ‘i’th row (without copying).
mean([axis])	Average the matrix over the given axis.
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
reshape(shape)
set_shape(shape)
setdiag(values[, k])	Fills the diagonal elements {a_ii} with the values from the given sequence.
sum([axis])	Sum the matrix over the given axis.
toarray([order, out])	See the docstring for spmatrix.toarray.
tobsr([blocksize])
tocoo()
tocsc()	Return Compressed Sparse Column format arrays for this matrix.
tocsr()	Return Compressed Sparse Row format arrays for this matrix.
todense([order, out])	Return a dense matrix representation of this matrix.
todia()
todok()
tolil([copy])
transpose()