scipy.sparse.

# lil_array#

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

Notes

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

• supports flexible slicing

• changes to the array sparsity structure are efficient

• 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`.

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

 `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. 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 Returns a copy of the 'i'th row. 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 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. `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__