# scipy.sparse.lil_matrix#

class scipy.sparse.lil_matrix(arg1, shape=None, dtype=None, copy=False)[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 a matrix efficiently, make sure the items are pre-sorted by index, per row.

This can be instantiated in several ways:
lil_array(D)

with a dense matrix or rank-2 ndarray D

lil_array(S)

with another sparse matrix S (equivalent to S.tolil())

lil_array((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.

• supports flexible slicing

• changes to the matrix sparsity structure are efficient

• 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:
dtypedtype

Data type of the matrix

`shape`2-tuple

Shape of the matrix

ndimint

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

Methods

 `__mul__`(other) `asformat`(format[, copy]) Return this array in the passed format. Upcast array to a floating point format (if necessary) `astype`(dtype[, casting, copy]) Cast the array elements to a specified type. `conj`([copy]) Element-wise complex conjugation. `conjugate`([copy]) Element-wise complex conjugation. Returns a copy of this array. Number of non-zero entries, equivalent to `diagonal`([k]) Returns the kth diagonal of the array. `dot`(other) Ordinary dot product Return the Hermitian transpose of this array. Get the shape of the matrix Returns a copy of column j of the array, as an (m x 1) sparse array (column vector). Matrix storage format Maximum number of elements to display when printed. `getnnz`([axis]) Number of stored values, including explicit zeros. Returns a copy of row i of the array, as a (1 x n) sparse array (row vector). Returns a view of the 'i'th row (without copying). `maximum`(other) Element-wise maximum between this and another array. `mean`([axis, dtype, out]) Compute the arithmetic mean along the specified axis. `minimum`(other) Element-wise minimum between this and another array. `multiply`(other) Point-wise multiplication by another array nonzero indices `power`(n[, dtype]) Element-wise power. `reshape`(self, shape[, order, copy]) Gives a new shape to a sparse array without changing its data. `resize`(*shape) Resize the array 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. `sum`([axis, dtype, out]) Sum the array elements over a given axis. `toarray`([order, out]) Return a dense ndarray representation of this sparse array. `tobsr`([blocksize, copy]) Convert this array to Block Sparse Row format. `tocoo`([copy]) Convert this array to COOrdinate format. `tocsc`([copy]) Convert this array to Compressed Sparse Column format. `tocsr`([copy]) Convert this array to Compressed Sparse Row format. `todense`([order, out]) Return a dense matrix representation of this sparse array. `todia`([copy]) Convert this array to sparse DIAgonal format. `todok`([copy]) Convert this array to Dictionary Of Keys format. `tolil`([copy]) Convert this array to List of Lists format. `trace`([offset]) Returns the sum along diagonals of the sparse array. `transpose`([axes, copy]) Reverses the dimensions of the sparse array.
 __getitem__