5.2.1.1.7. scipy.sparse.lil_matrix¶

class scipy.sparse.lil_matrix(arg1, shape=None, dtype=None, copy=False)[source]¶

Row-based linked list sparse 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.

Attributes

nnz	Get the count of explicitly-stored values (nonzeros)

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

__init__(arg1, shape=None, dtype=None, copy=False)[source]¶

Methods

__init__(arg1[, shape, dtype, copy])
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)	Ordinary dot product
getH()
get_shape()
getcol(j)	Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).
getformat()
getmaxprint()
getnnz([axis])	Get the count of explicitly-stored values (nonzeros)
getrow(i)	Returns a copy of the ‘i’th row.
getrowview(i)	Returns a view of the ‘i’th row (without copying).
maximum(other)
mean([axis])	Average the matrix over the given axis.
minimum(other)
multiply(other)	Point-wise multiplication by another matrix
nonzero()	nonzero indices
power(n[, dtype])
reshape(shape)
set_shape(shape)
setdiag(values[, k])	Set diagonal or off-diagonal elements of the array.
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()

Attributes

ndim
nnz	Get the count of explicitly-stored values (nonzeros)
shape