scipy.sparse.

lil_array

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

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

This can be instantiated in several ways:

lil_array(D)

where D is a 2-D ndarray

lil_array(S)

with another sparse array or matrix 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’.

Attributes:

dtypedtype

Data type of the array

shape2-tuple

Shape of the array

ndimint

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 array

rows

LIL format row index array of the array

T

Transpose.

Methods

__len__()
asformat(format[, copy])	Return this array/matrix in the passed format.
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()	Number of non-zero entries, equivalent to
diagonal([k])	Returns the kth diagonal of the array/matrix.
dot(other)	Ordinary dot product
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 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)	Point-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
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 array/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__
__mul__

Notes

Sparse arrays 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 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 matrix vector products (consider CSR or CSC)

Intended Usage

• LIL is a convenient format for constructing sparse arrays

• once an array has been constructed, convert to CSR or CSC format for fast arithmetic and matrix 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.