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Showing content from https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html below:

csc_matrix — SciPy v1.16.1 Manual

scipy.sparse.

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

Compressed Sparse Column matrix.

This can be instantiated in several ways:

csc_matrix(D)

where D is a 2-D ndarray

csc_matrix(S)

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

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

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

csc_matrix((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_matrix((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 in data[indptr[i]:indptr[i+1]]. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays.

Attributes:

dtypedtype

Data type of the matrix

shape2-tuple

Shape of the matrix

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 matrix

indices

CSC format index array of the matrix

indptr

CSC format index pointer array of the matrix

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 matrices 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_matrix
>>> csc_matrix((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_matrix((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_matrix((data, indices, indptr), shape=(3, 3)).toarray()
array([[1, 0, 4],
       [0, 0, 5],
       [2, 3, 6]])

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