`scipy.sparse._csc:csc_matrix`

source: build-install/usr/lib/python3.14/site-packages/scipy/sparse/_csc.py:305

Signature

def csc_matrix(*args, **kwargs)

Summary

Compressed Sparse Column matrix.

Extended Summary

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

data : ndarray: CSC format data array of the matrix
indices : ndarray: CSC format index array of the matrix
indptr : ndarray: CSC format index pointer array of the matrix
has_sorted_indices : bool: Whether indices are sorted
has_canonical_format : bool: Whether indices are sorted and no duplicate entries exist
dtype : dtype: Data type of the matrix
shape : 2-tuple: Shape of the matrix
ndim : int: Number of dimensions (this is always 2)
format : str: Three letter code for the format of the matrix storage, e.g. 'csc'
nnz : int: Number of values stored in the matrix
size : int: Number of values stored in the matrix
T : csc_matrix: The transpose of the matrix
mT : csc_matrix: The matrix transpose

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]])

Aliases

scipy.sparse.csc_matrix