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bundles / scipy 1.17.1 / scipy / sparse / _lil / lil_array

ABCMeta

scipy.sparse._lil:lil_array

source: /scipy/sparse/_lil.py :498

Signature

def   lil_array ( arg1 shape = None dtype = None copy = False * maxprint = None )

Summary

Row-based LIst of Lists sparse array.

Extended Summary

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

dtype : dtype

Data type of the array

shape : 2-tuple

Shape of the array

ndim : int

Number of dimensions (this is always 2)

nnz
size
data

LIL format data array of the array

rows

LIL format row index array of the array

T

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.

Aliases

  • scipy.sparse.lil_array

Referenced by

This package