# scipy.sparse.lil_matrix¶

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

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.

• 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: dtype : dtype Data type of the matrix shape : 2-tuple Get shape of a 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

Methods

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

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