sparse.csc

Compressed Sparse Column matrix format

Module Contents

Classes

csc_matrix() Compressed Sparse Column matrix

Functions

isspmatrix_csc(x) Is x of csc_matrix type?
class csc_matrix

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.
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 nonzero elements
data
Data array of the matrix
indices
CSC format index array
indptr
CSC format index pointer array
has_sorted_indices
Whether indices are sorted

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)
>>> 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]])
transpose(axes=None, copy=False)
__iter__()
tocsc(copy=False)
tocsr(copy=False)
__getitem__(key)
nonzero()
getrow(i)

Returns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector).

getcol(i)

Returns a copy of column i of the matrix, as a (m x 1) CSC matrix (column vector).

_swap(x)

swap the members of x if this is a column-oriented matrix

isspmatrix_csc(x)

Is x of csc_matrix type?

x
object to check for being a csc matrix
bool
True if x is a csc matrix, False otherwise
>>> from scipy.sparse import csc_matrix, isspmatrix_csc
>>> isspmatrix_csc(csc_matrix([[5]]))
True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc
>>> isspmatrix_csc(csr_matrix([[5]]))
False