csr_array — SciPy v1.16.2 Manual (original) (raw)

scipy.sparse.

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

Compressed Sparse Row array.

This can be instantiated in several ways:

csr_array(D)

where D is a 2-D ndarray

csr_array(S)

with another sparse array or matrix S (equivalent to S.tocsr())

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

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

csr_array((data, (row_ind, col_ind)), [shape=(M, N)])

where data, row_ind and col_ind satisfy the relationship a[row_ind[k], col_ind[k]] = data[k].

csr_array((data, indices, indptr), [shape=(M, N)])

is the standard CSR representation where the column indices for row i are stored in indices[indptr[i]:indptr[i+1]] and their corresponding values are stored in data[indptr[i]:indptr[i+1]]. If the shape parameter is not supplied, the array dimensions are inferred from the index arrays.

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

CSR format data array of the array

indices

CSR format index array of the array

indptr

CSR format index pointer array of the array

has_sorted_indices

Whether the indices are sorted

has_canonical_format

Whether the array/matrix has sorted indices and no duplicates

T

Transpose.

Methods

Notes

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

Advantages of the CSR format

• efficient arithmetic operations CSR + CSR, CSR * CSR, etc.
• efficient row slicing
• fast matrix vector products

Disadvantages of the CSR format

• slow column slicing operations (consider CSC)
• changes to the sparsity structure are expensive (consider LIL or DOK)

Canonical Format

• Within each row, indices are sorted by column.
• There are no duplicate entries.

Examples

import numpy as np from scipy.sparse import csr_array csr_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8)

row = np.array([0, 0, 1, 2, 2, 2]) col = np.array([0, 2, 2, 0, 1, 2]) data = np.array([1, 2, 3, 4, 5, 6]) csr_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]])

indptr = np.array([0, 2, 3, 6]) indices = np.array([0, 2, 2, 0, 1, 2]) data = np.array([1, 2, 3, 4, 5, 6]) csr_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]])

Duplicate entries are summed together:

row = np.array([0, 1, 2, 0]) col = np.array([0, 1, 1, 0]) data = np.array([1, 2, 4, 8]) csr_array((data, (row, col)), shape=(3, 3)).toarray() array([[9, 0, 0], [0, 2, 0], [0, 4, 0]])

As an example of how to construct a CSR array incrementally, the following snippet builds a term-document array from texts:

docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] indptr = [0] indices = [] data = [] vocabulary = {} for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... csr_array((data, indices, indptr), dtype=int).toarray() array([[2, 1, 0, 0], [0, 1, 1, 1]])