scipy.sparse.lil_matrix¶

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

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.

• 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

 shape ndim int(x[, base]) -> integer 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(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) getH() get_shape() getcol(j) Returns a copy of column j of the matrix, as an (m x 1) sparse getformat() getmaxprint() getnnz() getrow(i) Returns a copy of the ‘i’th row. getrowview(i) Returns a view of the ‘i’th row (without copying). mean([axis]) Average the matrix over the given axis. multiply(other) Point-wise multiplication by another matrix nonzero() nonzero indices reshape(shape) set_shape(shape) setdiag(values[, k]) Fills the diagonal elements {a_ii} with the values from the given sequence. sum([axis]) Sum the matrix over the given axis. 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() todia() todok() tolil([copy]) transpose()

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