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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.

  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:
    dtype dtype

      Data type of the matrix

    `shape` 2-tuple

      Shape of the matrix

    ndim int

      Number of dimensions (this is always 2)

    `nnz`

      Number of stored values, including explicit zeros.

    `size`

      Number of stored values.

    data

      LIL format data array of the matrix

    rows

      LIL format row index array of the matrix

    `T`

      Transpose.

  Methods

  `__len__`() |
  `__mul__`(other) |
  `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, equivalent to
  `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.

  __getitem__ |

  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`.