scipy.sparse.lil_matrix¶
-
class
scipy.sparse.
lil_matrix
(arg1, shape=None, dtype=None, copy=False)[source]¶ Row-based linked list 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_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.
- 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
.
- An array (
Attributes: Methods
asformat
(self, format[, copy])Return this matrix in the passed format. asfptype
(self)Upcast matrix to a floating point format (if necessary) astype
(self, dtype[, casting, copy])Cast the matrix elements to a specified type. conj
(self[, copy])Element-wise complex conjugation. conjugate
(self[, copy])Element-wise complex conjugation. copy
(self)Returns a copy of this matrix. count_nonzero
(self)Number of non-zero entries, equivalent to diagonal
(self[, k])Returns the k-th diagonal of the matrix. dot
(self, other)Ordinary dot product getH
(self)Return the Hermitian transpose of this matrix. get_shape
(self)Get shape of a matrix. getcol
(self, j)Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector). getformat
(self)Format of a matrix representation as a string. getmaxprint
(self)Maximum number of elements to display when printed. getnnz
(self[, axis])Number of stored values, including explicit zeros. getrow
(self, i)Returns a copy of the ‘i’th row. getrowview
(self, i)Returns a view of the ‘i’th row (without copying). maximum
(self, other)Element-wise maximum between this and another matrix. mean
(self[, axis, dtype, out])Compute the arithmetic mean along the specified axis. minimum
(self, other)Element-wise minimum between this and another matrix. multiply
(self, other)Point-wise multiplication by another matrix nonzero
(self)nonzero indices power
(self, n[, dtype])Element-wise power. reshape
(self, shape[, order, copy])Gives a new shape to a sparse matrix without changing its data. resize
(self, \*shape)Resize the matrix in-place to dimensions given by shape
set_shape
(self, shape)See reshape
.setdiag
(self, values[, k])Set diagonal or off-diagonal elements of the array. sum
(self[, axis, dtype, out])Sum the matrix elements over a given axis. toarray
(self[, order, out])Return a dense ndarray representation of this matrix. tobsr
(self[, blocksize, copy])Convert this matrix to Block Sparse Row format. tocoo
(self[, copy])Convert this matrix to COOrdinate format. tocsc
(self[, copy])Convert this matrix to Compressed Sparse Column format. tocsr
(self[, copy])Convert this matrix to Compressed Sparse Row format. todense
(self[, order, out])Return a dense matrix representation of this matrix. todia
(self[, copy])Convert this matrix to sparse DIAgonal format. todok
(self[, copy])Convert this matrix to Dictionary Of Keys format. tolil
(self[, copy])Convert this matrix to LInked List format. transpose
(self[, axes, copy])Reverses the dimensions of the sparse matrix.