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scipy.sparse.

# lil_matrix

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

  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 the matrix efficiently, make sure the items are pre-sorted by index, per row.

  Warning

  SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the spmatrix interface docs. For guidance on converting existing code to sparse arrays, see Migration from spmatrix to sparray.

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

      where D is a 2-D ndarray

    lil_matrix(S)

      with another sparse array or 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’.

  Attributes:
    data ndarray

      LIL format data array of the matrix

    rows ndarray

      LIL format row index array of the matrix

    dtype dtype

      Data type of the matrix

    `shape` 2-tuple

      Shape of the matrix

    ndim int

      Number of dimensions (this is always 2)

    `format` str

      Format string for matrix.

    `nnz` int

      Number of stored values, including explicit zeros.

    `size` int

      Number of stored values.

    `T` lil_matrix

      Transpose.

    `mT` lil_array

      Matrix transpose.

  Methods

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

  __add__ |
  __getitem__ |
  __matmul__ |
  __rmatmul__ |
  __rmul__ |
  __truediv__ |

  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`.