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.tocsc())
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
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
asfptype
astype
conj
conjugate
copy
diagonal
dot
getH
get_shape
getcol
getformat
getmaxprint
getnnz
getrow
getrowview
mean
multiply
nonzero
reshape
set_shape
setdiag
sum
toarray
tobsr
tocoo
tocsc
tocsr
todense
todia
todok
tolil
transpose

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