scipy.sparse.csc_matrix

class scipy.sparse.csc_matrix(arg1, shape=None, dtype=None, copy=False)

Compressed Sparse Column matrix

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)

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]])

Attributes

dtype
shape
ndim
nnz	Get the count of explicitly-stored values (nonzeros)
has_sorted_indices	Determine whether the matrix has sorted indices

data	Data array of the matrix
indices	CSC format index array
indptr	CSC format index pointer array

Methods