scipy.sparse.lil_array#

class scipy.sparse.lil_array(arg1, shape=None, dtype=None, copy=False)[source]#

Row-based list of lists sparse array

This is a structure for constructing sparse arrays incrementally. Note that inserting a single item can take linear time in the worst case; to construct a array efficiently, make sure the items are pre-sorted by index, per row.

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

with a dense array or rank-2 ndarray D

lil_array(S)

with another sparse array S (equivalent to S.tolil())

lil_array((M, N), [dtype])

to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype=’d’.

Notes

Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and array power.

Advantages of the LIL format

  • supports flexible slicing

  • changes to the array 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 array vector products (consider CSR or CSC)

Intended Usage

  • LIL is a convenient format for constructing sparse arrays

  • once a array has been constructed, convert to CSR or CSC format for fast arithmetic and array vector operations

  • consider using the COO format when constructing large arrays

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
dtypedtype

Data type of the array

shape2-tuple

Get shape of a matrix.

ndimint

Number of dimensions (this is always 2)

nnz

Number of stored values, including explicit zeros.

data

LIL format data array of the array

rows

LIL format row index array of the array

Methods

__len__()

__mul__(*args, **kwargs)

asformat(format[, copy])

Return this matrix in the passed format.

asfptype()

Upcast matrix to a floating point format (if necessary)

astype(dtype[, casting, copy])

Cast the matrix elements to a specified type.

conj([copy])

Element-wise complex conjugation.

conjugate([copy])

Element-wise complex conjugation.

copy()

Returns a copy of this matrix.

count_nonzero()

Number of non-zero entries, equivalent to

diagonal([k])

Returns the kth diagonal of the matrix.

dot(other)

Ordinary dot product

getH()

Return the Hermitian transpose of this matrix.

get_shape()

Get shape of a matrix.

getcol(j)

Returns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector).

getformat()

Format of a matrix representation as a string.

getmaxprint()

Maximum number of elements to display when printed.

getnnz([axis])

Number of stored values, including explicit zeros.

getrow(i)

Returns a copy of the 'i'th row.

getrowview(i)

Returns a view of the 'i'th row (without copying).

maximum(other)

Element-wise maximum between this and another matrix.

mean([axis, dtype, out])

Compute the arithmetic mean along the specified axis.

minimum(other)

Element-wise minimum between this and another matrix.

multiply(other)

Point-wise multiplication by another matrix

nonzero()

nonzero indices

power(n[, dtype])

Element-wise power.

reshape(self, shape[, order, copy])

Gives a new shape to a sparse matrix without changing its data.

resize(*shape)

Resize the matrix in-place to dimensions given by shape

set_shape(shape)

See reshape.

setdiag(values[, k])

Set diagonal or off-diagonal elements of the array.

sum([axis, dtype, out])

Sum the matrix elements over a given axis.

toarray([order, out])

Return a dense ndarray representation of this matrix.

tobsr([blocksize, copy])

Convert this matrix to Block Sparse Row format.

tocoo([copy])

Convert this matrix to COOrdinate format.

tocsc([copy])

Convert this matrix to Compressed Sparse Column format.

tocsr([copy])

Convert this matrix to Compressed Sparse Row format.

todense([order, out])

Return a dense matrix representation of this matrix.

todia([copy])

Convert this matrix to sparse DIAgonal format.

todok([copy])

Convert this matrix to Dictionary Of Keys format.

tolil([copy])

Convert this matrix to List of Lists format.

trace([offset])

Returns the sum along diagonals of the sparse matrix.

transpose([axes, copy])

Reverses the dimensions of the sparse matrix.

__getitem__