scipy.sparse.

lil_matrix#

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

Row-based LIst of Lists 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 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:
dtypedtype

Data type of the matrix

shape2-tuple

Shape of the matrix

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

size

Number of stored values.

data

LIL format data array of the matrix

rows

LIL format row index array of the matrix

T

Transpose.

Methods

__len__()

__mul__(other)

asformat(format[, copy])

Return this array/matrix in the passed format.

asfptype()

Upcast matrix to a floating point format (if necessary)

astype(dtype[, casting, copy])

Cast the array/matrix elements to a specified type.

conj([copy])

Element-wise complex conjugation.

conjugate([copy])

Element-wise complex conjugation.

copy()

Returns a copy of this array/matrix.

count_nonzero([axis])

Number of non-zero entries, equivalent to

diagonal([k])

Returns the kth diagonal of the array/matrix.

dot(other)

Ordinary dot product

getH()

Return the Hermitian transpose of this matrix.

get_shape()

Get the shape of the matrix

getcol(j)

Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).

getformat()

Matrix storage format

getmaxprint()

Maximum number of elements to display when printed.

getnnz([axis])

Number of stored values, including explicit zeros.

getrow(i)

Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector).

getrowview(i)

Returns a view of the 'i'th row (without copying).

maximum(other)

Element-wise maximum between this and another array/matrix.

mean([axis, dtype, out])

Compute the arithmetic mean along the specified axis.

minimum(other)

Element-wise minimum between this and another array/matrix.

multiply(other)

Point-wise multiplication by another array/matrix.

nonzero()

Nonzero indices of the array/matrix.

power(n[, dtype])

Element-wise power.

reshape(self, shape[, order, copy])

Gives a new shape to a sparse array/matrix without changing its data.

resize(*shape)

Resize the array/matrix in-place to dimensions given by shape

set_shape(shape)

Set the shape of the matrix in-place

setdiag(values[, k])

Set diagonal or off-diagonal elements of the array/matrix.

sum([axis, dtype, out])

Sum the array/matrix elements over a given axis.

toarray([order, out])

Return a dense ndarray representation of this sparse array/matrix.

tobsr([blocksize, copy])

Convert this array/matrix to Block Sparse Row format.

tocoo([copy])

Convert this array/matrix to COOrdinate format.

tocsc([copy])

Convert this array/matrix to Compressed Sparse Column format.

tocsr([copy])

Convert this array/matrix to Compressed Sparse Row format.

todense([order, out])

Return a dense representation of this sparse matrix.

todia([copy])

Convert this array/matrix to sparse DIAgonal format.

todok([copy])

Convert this array/matrix to Dictionary Of Keys format.

tolil([copy])

Convert this array/matrix to List of Lists format.

trace([offset])

Returns the sum along diagonals of the sparse array/matrix.

transpose([axes, copy])

Reverses the dimensions of the sparse array/matrix.

__getitem__

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.

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