csc_array — SciPy v1.16.0 Manual (original) (raw)

scipy.sparse.

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

Compressed Sparse Column array.

This can be instantiated in several ways:

csc_array(D)

where D is a 2-D ndarray

csc_array(S)

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

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

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

csc_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].

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

is the standard CSC representation where the row indices for column i are stored in indices[indptr[i]:indptr[i+1]]and their corresponding values are stored indata[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

CSC format data array of the array

indices

CSC format index array of the array

indptr

CSC 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 CSC format

• efficient arithmetic operations CSC + CSC, CSC * CSC, etc.
• efficient column slicing
• fast matrix vector products (CSR, BSR may be faster)

Disadvantages of the CSC format

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

Canonical format

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

Examples

import numpy as np from scipy.sparse import csc_array csc_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, 2, 2, 0, 1, 2]) col = np.array([0, 0, 1, 2, 2, 2]) data = np.array([1, 2, 3, 4, 5, 6]) csc_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 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]) csc_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 4], [0, 0, 5], [2, 3, 6]])