#### Previous topic

scipy.sparse.dok_matrix.viewvalues

#### Next topic

scipy.sparse.lil_matrix.nnz

# scipy.sparse.lil_matrix

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

  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`.

  Attributes

  `nnz` | Get the count of explicitly-stored values (nonzeros) :Parameters: axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row.

  dtype | (dtype) Data type of the matrix
  shape | (2-tuple) Shape of the matrix
  ndim | (int) Number of dimensions (this is always 2)
  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) | Ordinary dot product ..
  `getH`() |
  `get_shape`() |
  `getcol`(j) | Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).
  `getformat`() |
  `getmaxprint`() |
  `getnnz`([axis]) | Get the count of explicitly-stored values (nonzeros) :Parameters: axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row.
  `getrow`(i) | Returns a copy of the ‘i’th row.
  `getrowview`(i) | Returns a view of the ‘i’th row (without copying).
  `maximum`(other) |
  `mean`([axis]) | Average the matrix over the given axis.
  `minimum`(other) |
  `multiply`(other) | Point-wise multiplication by another matrix
  `nonzero`() | nonzero indices
  `power`(n[, dtype]) |
  `reshape`(shape) |
  `set_shape`(shape) |
  `setdiag`(values[, k]) | Set diagonal or off-diagonal elements of the array.
  `sum`([axis]) | Sum the matrix over the given axis.
  `toarray`([order, out]) | See the docstring for `spmatrix.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`([order, out]) | Return a dense matrix representation of this matrix.
  `todia`() |
  `todok`() |
  `tolil`([copy]) |
  `transpose`() |