Attributes

dtype : dtype

Data type of the matrix

shape : 2-tuple

Shape of the matrix

ndim : int

Number of dimensions (this is always 2)

nnz :

Number of stored values, including explicit zeros

data :

Data array of the matrix

indices :

CSC format index array

indptr :

CSC format index pointer array

has_sorted_indices :

Whether indices are sorted

This can be instantiated in several ways:

csc_matrix(D)

with a dense matrix or rank-2 ndarray D

csc_matrix(S)

with another sparse 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.

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)

Compressed Sparse Column matrix

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]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.sparse.linalg._dsolve.linsolve.spsolve scipy.sparse._matrix_io.load_npz scipy.sparse._csr.isspmatrix_csr scipy.sparse.linalg._isolve.iterative.gmres scipy.sparse._csc.csc_matrix scipy.sparse.linalg._dsolve.linsolve.spilu scipy.sparse.linalg._onenormest.onenormest scipy.sparse.linalg._matfuncs.inv scipy.sparse.linalg._eigen._svds.svds scipy.sparse.linalg._expm_multiply.expm_multiply scipy.sparse.linalg._isolve.lsqr.lsqr scipy.sparse.linalg._dsolve.linsolve.splu scipy.sparse.linalg._isolve.tfqmr.tfqmr scipy.sparse._coo.coo_matrix.tocsc scipy.sparse._matrix_io.save_npz scipy.sparse._csc.isspmatrix_csc scipy.sparse.linalg._isolve.minres.minres scipy.sparse.linalg._matfuncs.expm scipy.sparse.linalg._isolve.lsmr.lsmr scipy.sparse.linalg._isolve.iterative.qmr scipy.sparse.linalg._isolve.lgmres.lgmres

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GitHub : /scipy/sparse/_csc.py#16
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